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bert-base-uncased-test_2_100
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3742
- F1: {'f1': 0.8207293666026871}
- Accuracy: {'accuracy': 0.8132}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| No log | 1.0 | 7 | 0.6867 | {'f1': 0.6673895364597453} | {'accuracy': 0.5092} |
| No log | 2.0 | 14 | 0.6819 | {'f1': 0.5501760563380282} | {'accuracy': 0.5912} |
| No log | 3.0 | 21 | 0.6808 | {'f1': 0.37319468515309073} | {'accuracy': 0.566} |
| No log | 4.0 | 28 | 0.6778 | {'f1': 0.3979706877113867} | {'accuracy': 0.5728} |
| No log | 5.0 | 35 | 0.6748 | {'f1': 0.432258064516129} | {'accuracy': 0.5776} |
| No log | 6.0 | 42 | 0.6702 | {'f1': 0.5789250952179433} | {'accuracy': 0.602} |
| No log | 7.0 | 49 | 0.6664 | {'f1': 0.5185891325071497} | {'accuracy': 0.596} |
| No log | 8.0 | 56 | 0.6615 | {'f1': 0.5394378966455122} | {'accuracy': 0.5936} |
| No log | 9.0 | 63 | 0.6583 | {'f1': 0.5796124684077507} | {'accuracy': 0.6008} |
| No log | 10.0 | 70 | 0.6547 | {'f1': 0.628030303030303} | {'accuracy': 0.6072} |
| No log | 11.0 | 77 | 0.6429 | {'f1': 0.555812876331635} | {'accuracy': 0.6164} |
| No log | 12.0 | 84 | 0.6200 | {'f1': 0.6544731610337972} | {'accuracy': 0.6524} |
| No log | 13.0 | 91 | 0.6054 | {'f1': 0.6861480075901328} | {'accuracy': 0.6692} |
| No log | 14.0 | 98 | 0.5944 | {'f1': 0.6591107236268527} | {'accuracy': 0.6872} |
| No log | 15.0 | 105 | 0.5802 | {'f1': 0.6939109113199837} | {'accuracy': 0.7004} |
| No log | 16.0 | 112 | 0.5801 | {'f1': 0.7122069523039612} | {'accuracy': 0.7152} |
| No log | 17.0 | 119 | 0.5862 | {'f1': 0.7172413793103448} | {'accuracy': 0.7212} |
| No log | 18.0 | 126 | 0.6508 | {'f1': 0.7453769559032717} | {'accuracy': 0.7136} |
| No log | 19.0 | 133 | 0.5935 | {'f1': 0.7325581395348837} | {'accuracy': 0.7424} |
| No log | 20.0 | 140 | 0.6193 | {'f1': 0.7265029635901777} | {'accuracy': 0.7416} |
| No log | 21.0 | 147 | 0.6967 | {'f1': 0.7574221578566257} | {'accuracy': 0.732} |
| No log | 22.0 | 154 | 0.6781 | {'f1': 0.7065267001369236} | {'accuracy': 0.7428} |
| No log | 23.0 | 161 | 0.6566 | {'f1': 0.7692898272552784} | {'accuracy': 0.7596} |
| No log | 24.0 | 168 | 0.6656 | {'f1': 0.7717265353418308} | {'accuracy': 0.7636} |
| No log | 25.0 | 175 | 0.6746 | {'f1': 0.7662650602409639} | {'accuracy': 0.7672} |
| No log | 26.0 | 182 | 0.7001 | {'f1': 0.7759433962264151} | {'accuracy': 0.772} |
| No log | 27.0 | 189 | 0.7292 | {'f1': 0.7441063009001286} | {'accuracy': 0.7612} |
| No log | 28.0 | 196 | 0.7418 | {'f1': 0.7610474631751227} | {'accuracy': 0.7664} |
| No log | 29.0 | 203 | 0.7614 | {'f1': 0.751592356687898} | {'accuracy': 0.766} |
| No log | 30.0 | 210 | 0.7697 | {'f1': 0.7806022682831443} | {'accuracy': 0.7756} |
| No log | 31.0 | 217 | 0.7885 | {'f1': 0.7721265518622348} | {'accuracy': 0.7724} |
| No log | 32.0 | 224 | 0.8062 | {'f1': 0.7642209398186316} | {'accuracy': 0.7712} |
| No log | 33.0 | 231 | 0.8301 | {'f1': 0.7805456702253853} | {'accuracy': 0.778} |
| No log | 34.0 | 238 | 0.8503 | {'f1': 0.7807570977917981} | {'accuracy': 0.7776} |
| No log | 35.0 | 245 | 0.9258 | {'f1': 0.7845180498697432} | {'accuracy': 0.7684} |
| No log | 36.0 | 252 | 0.9121 | {'f1': 0.7879472693032015} | {'accuracy': 0.7748} |
| No log | 37.0 | 259 | 0.8719 | {'f1': 0.7829238824003222} | {'accuracy': 0.7844} |
| No log | 38.0 | 266 | 0.9147 | {'f1': 0.7897748950782144} | {'accuracy': 0.7796} |
| No log | 39.0 | 273 | 0.8983 | {'f1': 0.7862013638186923} | {'accuracy': 0.7868} |
| No log | 40.0 | 280 | 0.9294 | {'f1': 0.7913779830638953} | {'accuracy': 0.7832} |
| No log | 41.0 | 287 | 0.9203 | {'f1': 0.7841269841269841} | {'accuracy': 0.7824} |
| No log | 42.0 | 294 | 0.9434 | {'f1': 0.7949405902644691} | {'accuracy': 0.786} |
| No log | 43.0 | 301 | 0.9415 | {'f1': 0.7944465869649053} | {'accuracy': 0.7868} |
| No log | 44.0 | 308 | 0.9479 | {'f1': 0.770859805167302} | {'accuracy': 0.7836} |
| No log | 45.0 | 315 | 0.9805 | {'f1': 0.7955927051671733} | {'accuracy': 0.7848} |
| No log | 46.0 | 322 | 0.9753 | {'f1': 0.788184998056743} | {'accuracy': 0.782} |
| No log | 47.0 | 329 | 0.9732 | {'f1': 0.7798537774167345} | {'accuracy': 0.7832} |
| No log | 48.0 | 336 | 1.0218 | {'f1': 0.7910163684811572} | {'accuracy': 0.7804} |
| No log | 49.0 | 343 | 1.0071 | {'f1': 0.7824056052938886} | {'accuracy': 0.7764} |
| No log | 50.0 | 350 | 0.9941 | {'f1': 0.7769962763756723} | {'accuracy': 0.7844} |
| No log | 51.0 | 357 | 1.1072 | {'f1': 0.7849580138736765} | {'accuracy': 0.7644} |
| No log | 52.0 | 364 | 1.0659 | {'f1': 0.7905048982667672} | {'accuracy': 0.7776} |
| No log | 53.0 | 371 | 1.0176 | {'f1': 0.7758268681094325} | {'accuracy': 0.7804} |
| No log | 54.0 | 378 | 1.0482 | {'f1': 0.7857695282289249} | {'accuracy': 0.7784} |
| No log | 55.0 | 385 | 1.2158 | {'f1': 0.784452296819788} | {'accuracy': 0.756} |
| No log | 56.0 | 392 | 1.1118 | {'f1': 0.7880575009214891} | {'accuracy': 0.77} |
| No log | 57.0 | 399 | 1.0318 | {'f1': 0.7878308968787041} | {'accuracy': 0.7852} |
| No log | 58.0 | 406 | 1.0296 | {'f1': 0.7861178369652946} | {'accuracy': 0.788} |
| No log | 59.0 | 413 | 1.1107 | {'f1': 0.7899034892353377} | {'accuracy': 0.7736} |
| No log | 60.0 | 420 | 1.0667 | {'f1': 0.791124713083397} | {'accuracy': 0.7816} |
| No log | 61.0 | 427 | 1.0478 | {'f1': 0.7916666666666666} | {'accuracy': 0.788} |
| No log | 62.0 | 434 | 1.0506 | {'f1': 0.7908625443087829} | {'accuracy': 0.7876} |
| No log | 63.0 | 441 | 1.0569 | {'f1': 0.7927786499215072} | {'accuracy': 0.7888} |
| No log | 64.0 | 448 | 1.0732 | {'f1': 0.7882534775888718} | {'accuracy': 0.7808} |
| No log | 65.0 | 455 | 1.0744 | {'f1': 0.7902287708414115} | {'accuracy': 0.7836} |
| No log | 66.0 | 462 | 1.0650 | {'f1': 0.7919463087248323} | {'accuracy': 0.7892} |
| No log | 67.0 | 469 | 1.1210 | {'f1': 0.7916981132075471} | {'accuracy': 0.7792} |
| No log | 68.0 | 476 | 1.0886 | {'f1': 0.7925552539744086} | {'accuracy': 0.786} |
| No log | 69.0 | 483 | 1.0712 | {'f1': 0.7895372233400404} | {'accuracy': 0.7908} |
| No log | 70.0 | 490 | 1.0749 | {'f1': 0.7860897695107156} | {'accuracy': 0.7884} |
| No log | 71.0 | 497 | 1.0807 | {'f1': 0.7931446791550419} | {'accuracy': 0.7924} |
| 0.1431 | 72.0 | 504 | 1.0837 | {'f1': 0.7931446791550419} | {'accuracy': 0.7924} |
| 0.1431 | 73.0 | 511 | 1.0897 | {'f1': 0.7936758893280632} | {'accuracy': 0.7912} |
| 0.1431 | 74.0 | 518 | 1.0925 | {'f1': 0.7952755905511811} | {'accuracy': 0.792} |
| 0.1431 | 75.0 | 525 | 1.1018 | {'f1': 0.7951713395638628} | {'accuracy': 0.7896} |
| 0.1431 | 76.0 | 532 | 1.1121 | {'f1': 0.7938104448742745} | {'accuracy': 0.7868} |
| 0.1431 | 77.0 | 539 | 1.1071 | {'f1': 0.7945631067961165} | {'accuracy': 0.7884} |
| 0.1431 | 78.0 | 546 | 1.1149 | {'f1': 0.7944250871080138} | {'accuracy': 0.7876} |
| 0.1431 | 79.0 | 553 | 1.1702 | {'f1': 0.7919312663429211} | {'accuracy': 0.7772} |
| 0.1431 | 80.0 | 560 | 1.1048 | {'f1': 0.7970277669143527} | {'accuracy': 0.7924} |
| 0.1431 | 81.0 | 567 | 1.0988 | {'f1': 0.7942583732057418} | {'accuracy': 0.7936} |
| 0.1431 | 82.0 | 574 | 1.1094 | {'f1': 0.797141722905915} | {'accuracy': 0.7956} |
| 0.1431 | 83.0 | 581 | 1.1293 | {'f1': 0.79408330089529} | {'accuracy': 0.7884} |
| 0.1431 | 84.0 | 588 | 1.1591 | {'f1': 0.7948229920060906} | {'accuracy': 0.7844} |
| 0.1431 | 85.0 | 595 | 1.1706 | {'f1': 0.7921241953805376} | {'accuracy': 0.7804} |
| 0.1431 | 86.0 | 602 | 1.1557 | {'f1': 0.792467332820907} | {'accuracy': 0.784} |
| 0.1431 | 87.0 | 609 | 1.1554 | {'f1': 0.76732249786142} | {'accuracy': 0.7824} |
| 0.1431 | 88.0 | 616 | 1.1516 | {'f1': 0.7946257197696737} | {'accuracy': 0.786} |
| 0.1431 | 89.0 | 623 | 1.2337 | {'f1': 0.7969208211143696} | {'accuracy': 0.7784} |
| 0.1431 | 90.0 | 630 | 1.1372 | {'f1': 0.7978227060653188} | {'accuracy': 0.792} |
| 0.1431 | 91.0 | 637 | 1.1228 | {'f1': 0.7916833266693323} | {'accuracy': 0.7916} |
| 0.1431 | 92.0 | 644 | 1.1289 | {'f1': 0.7952569169960475} | {'accuracy': 0.7928} |
| 0.1431 | 93.0 | 651 | 1.1409 | {'f1': 0.7992187500000001} | {'accuracy': 0.7944} |
| 0.1431 | 94.0 | 658 | 1.1469 | {'f1': 0.7989109295993777} | {'accuracy': 0.7932} |
| 0.1431 | 95.0 | 665 | 1.2357 | {'f1': 0.7549019607843137} | {'accuracy': 0.78} |
| 0.1431 | 96.0 | 672 | 1.1278 | {'f1': 0.789664917238595} | {'accuracy': 0.7916} |
| 0.1431 | 97.0 | 679 | 1.1492 | {'f1': 0.8013937282229966} | {'accuracy': 0.7948} |
| 0.1431 | 98.0 | 686 | 1.1501 | {'f1': 0.7805486284289276} | {'accuracy': 0.7888} |
| 0.1431 | 99.0 | 693 | 1.1785 | {'f1': 0.7683807904802381} | {'accuracy': 0.782} |
| 0.1431 | 100.0 | 700 | 1.1602 | {'f1': 0.7807708246995443} | {'accuracy': 0.7884} |
| 0.1431 | 101.0 | 707 | 1.1585 | {'f1': 0.7963621984974298} | {'accuracy': 0.794} |
| 0.1431 | 102.0 | 714 | 1.2049 | {'f1': 0.7948523845571537} | {'accuracy': 0.7832} |
| 0.1431 | 103.0 | 721 | 1.1969 | {'f1': 0.7960275019098548} | {'accuracy': 0.7864} |
| 0.1431 | 104.0 | 728 | 1.1693 | {'f1': 0.7960552268244576} | {'accuracy': 0.7932} |
| 0.1431 | 105.0 | 735 | 1.1664 | {'f1': 0.7934739355352168} | {'accuracy': 0.7924} |
| 0.1431 | 106.0 | 742 | 1.1675 | {'f1': 0.7937898089171975} | {'accuracy': 0.7928} |
| 0.1431 | 107.0 | 749 | 1.1750 | {'f1': 0.7965299684542587} | {'accuracy': 0.7936} |
| 0.1431 | 108.0 | 756 | 1.1829 | {'f1': 0.7989045383411582} | {'accuracy': 0.7944} |
| 0.1431 | 109.0 | 763 | 1.1870 | {'f1': 0.797818465134398} | {'accuracy': 0.7924} |
| 0.1431 | 110.0 | 770 | 1.1873 | {'f1': 0.7987519500780031} | {'accuracy': 0.7936} |
| 0.1431 | 111.0 | 777 | 1.1899 | {'f1': 0.798443579766537} | {'accuracy': 0.7928} |
| 0.1431 | 112.0 | 784 | 1.2010 | {'f1': 0.798151001540832} | {'accuracy': 0.7904} |
| 0.1431 | 113.0 | 791 | 1.1904 | {'f1': 0.799532892175944} | {'accuracy': 0.794} |
| 0.1431 | 114.0 | 798 | 1.1816 | {'f1': 0.7965299684542587} | {'accuracy': 0.7936} |
| 0.1431 | 115.0 | 805 | 1.1729 | {'f1': 0.7906413876563132} | {'accuracy': 0.7924} |
| 0.1431 | 116.0 | 812 | 1.1751 | {'f1': 0.7868453105968332} | {'accuracy': 0.79} |
| 0.1431 | 117.0 | 819 | 1.1747 | {'f1': 0.7909604519774011} | {'accuracy': 0.7928} |
| 0.1431 | 118.0 | 826 | 1.1807 | {'f1': 0.7957244655581948} | {'accuracy': 0.7936} |
| 0.1431 | 119.0 | 833 | 1.3983 | {'f1': 0.7960199004975125} | {'accuracy': 0.7704} |
| 0.1431 | 120.0 | 840 | 1.3032 | {'f1': 0.7992700729927008} | {'accuracy': 0.78} |
| 0.1431 | 121.0 | 847 | 1.2420 | {'f1': 0.7653997378768019} | {'accuracy': 0.7852} |
| 0.1431 | 122.0 | 854 | 1.1608 | {'f1': 0.7954911433172303} | {'accuracy': 0.7968} |
| 0.1431 | 123.0 | 861 | 1.2434 | {'f1': 0.8047512991833704} | {'accuracy': 0.7896} |
| 0.1431 | 124.0 | 868 | 1.1561 | {'f1': 0.7962662337662338} | {'accuracy': 0.7992} |
| 0.1431 | 125.0 | 875 | 1.1961 | {'f1': 0.7776355100298763} | {'accuracy': 0.7916} |
| 0.1431 | 126.0 | 882 | 1.2566 | {'f1': 0.802962962962963} | {'accuracy': 0.7872} |
| 0.1431 | 127.0 | 889 | 1.1969 | {'f1': 0.8042813455657493} | {'accuracy': 0.7952} |
| 0.1431 | 128.0 | 896 | 1.1668 | {'f1': 0.7972480777013354} | {'accuracy': 0.7996} |
| 0.1431 | 129.0 | 903 | 1.1762 | {'f1': 0.7916152897657213} | {'accuracy': 0.7972} |
| 0.1431 | 130.0 | 910 | 1.1758 | {'f1': 0.791307913079131} | {'accuracy': 0.7964} |
| 0.1431 | 131.0 | 917 | 1.1774 | {'f1': 0.8007889546351085} | {'accuracy': 0.798} |
| 0.1431 | 132.0 | 924 | 1.2013 | {'f1': 0.8047564250095894} | {'accuracy': 0.7964} |
| 0.1431 | 133.0 | 931 | 1.2061 | {'f1': 0.805045871559633} | {'accuracy': 0.796} |
| 0.1431 | 134.0 | 938 | 1.1958 | {'f1': 0.8041714947856314} | {'accuracy': 0.7972} |
| 0.1431 | 135.0 | 945 | 1.1887 | {'f1': 0.8040514218932606} | {'accuracy': 0.7988} |
| 0.1431 | 136.0 | 952 | 1.1840 | {'f1': 0.8040832351786416} | {'accuracy': 0.8004} |
| 0.1431 | 137.0 | 959 | 1.1836 | {'f1': 0.8056648308418568} | {'accuracy': 0.8024} |
| 0.1431 | 138.0 | 966 | 1.1792 | {'f1': 0.8033175355450237} | {'accuracy': 0.8008} |
| 0.1431 | 139.0 | 973 | 1.1881 | {'f1': 0.8073322932917315} | {'accuracy': 0.8024} |
| 0.1431 | 140.0 | 980 | 1.2032 | {'f1': 0.8058551617873652} | {'accuracy': 0.7984} |
| 0.1431 | 141.0 | 987 | 1.2021 | {'f1': 0.8070987654320988} | {'accuracy': 0.8} |
| 0.1431 | 142.0 | 994 | 1.2005 | {'f1': 0.8061895551257253} | {'accuracy': 0.7996} |
| 0.0009 | 143.0 | 1001 | 1.1952 | {'f1': 0.8074679113185532} | {'accuracy': 0.802} |
| 0.0009 | 144.0 | 1008 | 1.1926 | {'f1': 0.8085937499999999} | {'accuracy': 0.804} |
| 0.0009 | 145.0 | 1015 | 1.1915 | {'f1': 0.8079780993351583} | {'accuracy': 0.8036} |
| 0.0009 | 146.0 | 1022 | 1.1910 | {'f1': 0.8067424539396315} | {'accuracy': 0.8028} |
| 0.0009 | 147.0 | 1029 | 1.1865 | {'f1': 0.806948282668772} | {'accuracy': 0.8044} |
| 0.0009 | 148.0 | 1036 | 1.1827 | {'f1': 0.8025528520143598} | {'accuracy': 0.802} |
| 0.0009 | 149.0 | 1043 | 1.1839 | {'f1': 0.8004866180048661} | {'accuracy': 0.8032} |
| 0.0009 | 150.0 | 1050 | 1.1840 | {'f1': 0.8009708737864079} | {'accuracy': 0.8032} |
| 0.0009 | 151.0 | 1057 | 1.1846 | {'f1': 0.8025682182985554} | {'accuracy': 0.8032} |
| 0.0009 | 152.0 | 1064 | 1.1869 | {'f1': 0.8039840637450199} | {'accuracy': 0.8032} |
| 0.0009 | 153.0 | 1071 | 1.1888 | {'f1': 0.8044515103338633} | {'accuracy': 0.8032} |
| 0.0009 | 154.0 | 1078 | 1.2019 | {'f1': 0.8078124999999999} | {'accuracy': 0.8032} |
| 0.0009 | 155.0 | 1085 | 1.2122 | {'f1': 0.8083785880527542} | {'accuracy': 0.8024} |
| 0.0009 | 156.0 | 1092 | 1.2193 | {'f1': 0.8083462132921175} | {'accuracy': 0.8016} |
| 0.0009 | 157.0 | 1099 | 1.2198 | {'f1': 0.8083462132921175} | {'accuracy': 0.8016} |
| 0.0009 | 158.0 | 1106 | 1.2121 | {'f1': 0.8087261394624076} | {'accuracy': 0.8036} |
| 0.0009 | 159.0 | 1113 | 1.2084 | {'f1': 0.8078124999999999} | {'accuracy': 0.8032} |
| 0.0009 | 160.0 | 1120 | 1.2091 | {'f1': 0.8078124999999999} | {'accuracy': 0.8032} |
| 0.0009 | 161.0 | 1127 | 1.2117 | {'f1': 0.8074970714564623} | {'accuracy': 0.8028} |
| 0.0009 | 162.0 | 1134 | 1.2270 | {'f1': 0.7828668363019508} | {'accuracy': 0.7952} |
| 0.0009 | 163.0 | 1141 | 1.2069 | {'f1': 0.8028503562945369} | {'accuracy': 0.8008} |
| 0.0009 | 164.0 | 1148 | 1.4732 | {'f1': 0.8007054673721341} | {'accuracy': 0.774} |
| 0.0009 | 165.0 | 1155 | 1.2911 | {'f1': 0.8055451479955038} | {'accuracy': 0.7924} |
| 0.0009 | 166.0 | 1162 | 1.2061 | {'f1': 0.8075709779179809} | {'accuracy': 0.8048} |
| 0.0009 | 167.0 | 1169 | 1.2534 | {'f1': 0.8086070215175539} | {'accuracy': 0.7972} |
| 0.0009 | 168.0 | 1176 | 1.2814 | {'f1': 0.8092744951383695} | {'accuracy': 0.796} |
| 0.0009 | 169.0 | 1183 | 1.2533 | {'f1': 0.8111361926260346} | {'accuracy': 0.7992} |
| 0.0009 | 170.0 | 1190 | 1.2007 | {'f1': 0.8126959247648903} | {'accuracy': 0.8088} |
| 0.0009 | 171.0 | 1197 | 1.1935 | {'f1': 0.8106180665610143} | {'accuracy': 0.8088} |
| 0.0009 | 172.0 | 1204 | 1.1932 | {'f1': 0.8079522862823061} | {'accuracy': 0.8068} |
| 0.0009 | 173.0 | 1211 | 1.1938 | {'f1': 0.8079522862823061} | {'accuracy': 0.8068} |
| 0.0009 | 174.0 | 1218 | 1.1952 | {'f1': 0.8095238095238094} | {'accuracy': 0.808} |
| 0.0009 | 175.0 | 1225 | 1.1973 | {'f1': 0.8118577075098814} | {'accuracy': 0.8096} |
| 0.0009 | 176.0 | 1232 | 1.2001 | {'f1': 0.8123028391167193} | {'accuracy': 0.8096} |
| 0.0009 | 177.0 | 1239 | 1.2003 | {'f1': 0.8126232741617356} | {'accuracy': 0.81} |
| 0.0009 | 178.0 | 1246 | 1.1996 | {'f1': 0.8104678826328311} | {'accuracy': 0.8088} |
| 0.0009 | 179.0 | 1253 | 1.1999 | {'f1': 0.8095238095238094} | {'accuracy': 0.808} |
| 0.0009 | 180.0 | 1260 | 1.2009 | {'f1': 0.8104678826328311} | {'accuracy': 0.8088} |
| 0.0009 | 181.0 | 1267 | 1.2028 | {'f1': 0.8126482213438735} | {'accuracy': 0.8104} |
| 0.0009 | 182.0 | 1274 | 1.2050 | {'f1': 0.8130914826498422} | {'accuracy': 0.8104} |
| 0.0009 | 183.0 | 1281 | 1.2959 | {'f1': 0.8094170403587443} | {'accuracy': 0.796} |
| 0.0009 | 184.0 | 1288 | 1.4564 | {'f1': 0.8015647226173542} | {'accuracy': 0.7768} |
| 0.0009 | 185.0 | 1295 | 1.2213 | {'f1': 0.8090154211150652} | {'accuracy': 0.8068} |
| 0.0009 | 186.0 | 1302 | 1.2472 | {'f1': 0.7836355967946014} | {'accuracy': 0.7948} |
| 0.0009 | 187.0 | 1309 | 1.2286 | {'f1': 0.8066561014263074} | {'accuracy': 0.8048} |
| 0.0009 | 188.0 | 1316 | 1.2583 | {'f1': 0.8121866563825684} | {'accuracy': 0.8052} |
| 0.0009 | 189.0 | 1323 | 1.2744 | {'f1': 0.8105423987776929} | {'accuracy': 0.8016} |
| 0.0009 | 190.0 | 1330 | 1.2877 | {'f1': 0.8078967350037963} | {'accuracy': 0.7976} |
| 0.0009 | 191.0 | 1337 | 1.2626 | {'f1': 0.8108317214700194} | {'accuracy': 0.8044} |
| 0.0009 | 192.0 | 1344 | 1.2989 | {'f1': 0.7748058671268335} | {'accuracy': 0.7912} |
| 0.0009 | 193.0 | 1351 | 1.2673 | {'f1': 0.7831174258253238} | {'accuracy': 0.7924} |
| 0.0009 | 194.0 | 1358 | 1.2525 | {'f1': 0.8090332805071315} | {'accuracy': 0.8072} |
| 0.0009 | 195.0 | 1365 | 1.2736 | {'f1': 0.810077519379845} | {'accuracy': 0.804} |
| 0.0009 | 196.0 | 1372 | 1.3521 | {'f1': 0.8102297998517419} | {'accuracy': 0.7952} |
| 0.0009 | 197.0 | 1379 | 1.3654 | {'f1': 0.8086828550404709} | {'accuracy': 0.792} |
| 0.0009 | 198.0 | 1386 | 1.3538 | {'f1': 0.8093126385809312} | {'accuracy': 0.7936} |
| 0.0009 | 199.0 | 1393 | 1.2624 | {'f1': 0.8131782945736433} | {'accuracy': 0.8072} |
| 0.0009 | 200.0 | 1400 | 1.2467 | {'f1': 0.7957166392092258} | {'accuracy': 0.8016} |
| 0.0009 | 201.0 | 1407 | 1.2774 | {'f1': 0.7833474936278675} | {'accuracy': 0.796} |
| 0.0009 | 202.0 | 1414 | 1.2753 | {'f1': 0.7833827893175075} | {'accuracy': 0.7956} |
| 0.0009 | 203.0 | 1421 | 1.2851 | {'f1': 0.8121398386477141} | {'accuracy': 0.8044} |
| 0.0009 | 204.0 | 1428 | 1.4365 | {'f1': 0.8037585833032164} | {'accuracy': 0.7828} |
| 0.0009 | 205.0 | 1435 | 1.4102 | {'f1': 0.8037997807818781} | {'accuracy': 0.7852} |
| 0.0009 | 206.0 | 1442 | 1.3754 | {'f1': 0.8053293856402663} | {'accuracy': 0.7896} |
| 0.0009 | 207.0 | 1449 | 1.3527 | {'f1': 0.8046407185628742} | {'accuracy': 0.7912} |
| 0.0009 | 208.0 | 1456 | 1.3362 | {'f1': 0.8088955898982284} | {'accuracy': 0.7972} |
| 0.0009 | 209.0 | 1463 | 1.3206 | {'f1': 0.8138561096307575} | {'accuracy': 0.8044} |
| 0.0009 | 210.0 | 1470 | 1.3094 | {'f1': 0.8134814247414783} | {'accuracy': 0.8052} |
| 0.0009 | 211.0 | 1477 | 1.3024 | {'f1': 0.813389765294344} | {'accuracy': 0.806} |
| 0.0009 | 212.0 | 1484 | 1.2958 | {'f1': 0.8108317214700194} | {'accuracy': 0.8044} |
| 0.0009 | 213.0 | 1491 | 1.2930 | {'f1': 0.8102444703143191} | {'accuracy': 0.8044} |
| 0.0009 | 214.0 | 1498 | 1.2977 | {'f1': 0.8108317214700194} | {'accuracy': 0.8044} |
| 0.0003 | 215.0 | 1505 | 1.2979 | {'f1': 0.8109992254066616} | {'accuracy': 0.8048} |
| 0.0003 | 216.0 | 1512 | 1.3123 | {'f1': 0.8141321044546852} | {'accuracy': 0.8064} |
| 0.0003 | 217.0 | 1519 | 1.3245 | {'f1': 0.8129770992366412} | {'accuracy': 0.804} |
| 0.0003 | 218.0 | 1526 | 1.3279 | {'f1': 0.8126669210225105} | {'accuracy': 0.8036} |
| 0.0003 | 219.0 | 1533 | 1.3249 | {'f1': 0.813287514318442} | {'accuracy': 0.8044} |
| 0.0003 | 220.0 | 1540 | 1.3202 | {'f1': 0.8147013782542114} | {'accuracy': 0.8064} |
| 0.0003 | 221.0 | 1547 | 1.3125 | {'f1': 0.8112480739599385} | {'accuracy': 0.804} |
| 0.0003 | 222.0 | 1554 | 1.3040 | {'f1': 0.8105385509492445} | {'accuracy': 0.8044} |
| 0.0003 | 223.0 | 1561 | 1.3616 | {'f1': 0.8061492313460817} | {'accuracy': 0.7932} |
| 0.0003 | 224.0 | 1568 | 1.6007 | {'f1': 0.7990196078431372} | {'accuracy': 0.7704} |
| 0.0003 | 225.0 | 1575 | 1.5556 | {'f1': 0.8007054673721341} | {'accuracy': 0.774} |
| 0.0003 | 226.0 | 1582 | 1.4173 | {'f1': 0.8058608058608058} | {'accuracy': 0.788} |
| 0.0003 | 227.0 | 1589 | 1.2708 | {'f1': 0.8091844813935075} | {'accuracy': 0.8072} |
| 0.0003 | 228.0 | 1596 | 1.2721 | {'f1': 0.7967145790554415} | {'accuracy': 0.802} |
| 0.0003 | 229.0 | 1603 | 1.2797 | {'f1': 0.7948611686697058} | {'accuracy': 0.802} |
| 0.0003 | 230.0 | 1610 | 1.2756 | {'f1': 0.7977020927369718} | {'accuracy': 0.8028} |
| 0.0003 | 231.0 | 1617 | 1.2732 | {'f1': 0.7987012987012987} | {'accuracy': 0.8016} |
| 0.0003 | 232.0 | 1624 | 1.2735 | {'f1': 0.8037007240547064} | {'accuracy': 0.8048} |
| 0.0003 | 233.0 | 1631 | 1.2756 | {'f1': 0.8060775689724111} | {'accuracy': 0.806} |
| 0.0003 | 234.0 | 1638 | 1.2775 | {'f1': 0.8087649402390439} | {'accuracy': 0.808} |
| 0.0003 | 235.0 | 1645 | 1.2786 | {'f1': 0.8084428514536042} | {'accuracy': 0.8076} |
| 0.0003 | 236.0 | 1652 | 1.2803 | {'f1': 0.8068362480127186} | {'accuracy': 0.8056} |
| 0.0003 | 237.0 | 1659 | 1.2827 | {'f1': 0.8076009501187648} | {'accuracy': 0.8056} |
| 0.0003 | 238.0 | 1666 | 1.2816 | {'f1': 0.8071570576540756} | {'accuracy': 0.806} |
| 0.0003 | 239.0 | 1673 | 1.2808 | {'f1': 0.8068635275339185} | {'accuracy': 0.8064} |
| 0.0003 | 240.0 | 1680 | 1.2807 | {'f1': 0.8065547561950439} | {'accuracy': 0.8064} |
| 0.0003 | 241.0 | 1687 | 1.2794 | {'f1': 0.8032193158953722} | {'accuracy': 0.8044} |
| 0.0003 | 242.0 | 1694 | 1.2994 | {'f1': 0.791578947368421} | {'accuracy': 0.802} |
| 0.0003 | 243.0 | 1701 | 1.3223 | {'f1': 0.7840616966580977} | {'accuracy': 0.7984} |
| 0.0003 | 244.0 | 1708 | 1.2878 | {'f1': 0.7956810631229236} | {'accuracy': 0.8032} |
| 0.0003 | 245.0 | 1715 | 1.2761 | {'f1': 0.8040567951318459} | {'accuracy': 0.8068} |
| 0.0003 | 246.0 | 1722 | 1.2763 | {'f1': 0.8051323175621492} | {'accuracy': 0.8056} |
| 0.0003 | 247.0 | 1729 | 1.2789 | {'f1': 0.810207336523126} | {'accuracy': 0.8096} |
| 0.0003 | 248.0 | 1736 | 1.2818 | {'f1': 0.8109393579072532} | {'accuracy': 0.8092} |
| 0.0003 | 249.0 | 1743 | 1.2847 | {'f1': 0.8138801261829653} | {'accuracy': 0.8112} |
| 0.0003 | 250.0 | 1750 | 1.2864 | {'f1': 0.8140267927501971} | {'accuracy': 0.8112} |
| 0.0003 | 251.0 | 1757 | 1.2869 | {'f1': 0.8140267927501971} | {'accuracy': 0.8112} |
| 0.0003 | 252.0 | 1764 | 1.2863 | {'f1': 0.8132649032767469} | {'accuracy': 0.8108} |
| 0.0003 | 253.0 | 1771 | 1.2859 | {'f1': 0.8117088607594937} | {'accuracy': 0.8096} |
| 0.0003 | 254.0 | 1778 | 1.2860 | {'f1': 0.811089108910891} | {'accuracy': 0.8092} |
| 0.0003 | 255.0 | 1785 | 1.2867 | {'f1': 0.81203007518797} | {'accuracy': 0.81} |
| 0.0003 | 256.0 | 1792 | 1.2884 | {'f1': 0.8132649032767469} | {'accuracy': 0.8108} |
| 0.0003 | 257.0 | 1799 | 1.2988 | {'f1': 0.8167252833137943} | {'accuracy': 0.8124} |
| 0.0003 | 258.0 | 1806 | 1.3067 | {'f1': 0.8163424124513619} | {'accuracy': 0.8112} |
| 0.0003 | 259.0 | 1813 | 1.2974 | {'f1': 0.8155111633372502} | {'accuracy': 0.8116} |
| 0.0003 | 260.0 | 1820 | 1.2927 | {'f1': 0.8144654088050315} | {'accuracy': 0.8112} |
| 0.0003 | 261.0 | 1827 | 1.2901 | {'f1': 0.8127962085308058} | {'accuracy': 0.8104} |
| 0.0003 | 262.0 | 1834 | 1.2891 | {'f1': 0.8126732673267326} | {'accuracy': 0.8108} |
| 0.0003 | 263.0 | 1841 | 1.2890 | {'f1': 0.8107893692978976} | {'accuracy': 0.8092} |
| 0.0003 | 264.0 | 1848 | 1.2912 | {'f1': 0.8127962085308058} | {'accuracy': 0.8104} |
| 0.0003 | 265.0 | 1855 | 1.2928 | {'f1': 0.8142011834319528} | {'accuracy': 0.8116} |
| 0.0003 | 266.0 | 1862 | 1.2935 | {'f1': 0.8138801261829653} | {'accuracy': 0.8112} |
| 0.0003 | 267.0 | 1869 | 1.2941 | {'f1': 0.814814814814815} | {'accuracy': 0.812} |
| 0.0003 | 268.0 | 1876 | 1.2942 | {'f1': 0.8138801261829653} | {'accuracy': 0.8112} |
| 0.0003 | 269.0 | 1883 | 1.2951 | {'f1': 0.8144938952343442} | {'accuracy': 0.8116} |
| 0.0003 | 270.0 | 1890 | 1.2983 | {'f1': 0.8141453831041258} | {'accuracy': 0.8108} |
| 0.0003 | 271.0 | 1897 | 1.3002 | {'f1': 0.8142913231252454} | {'accuracy': 0.8108} |
| 0.0003 | 272.0 | 1904 | 1.3017 | {'f1': 0.8156862745098038} | {'accuracy': 0.812} |
| 0.0003 | 273.0 | 1911 | 1.3045 | {'f1': 0.8161189358372457} | {'accuracy': 0.812} |
| 0.0003 | 274.0 | 1918 | 1.3077 | {'f1': 0.8175068386088317} | {'accuracy': 0.8132} |
| 0.0003 | 275.0 | 1925 | 1.3098 | {'f1': 0.8173302107728336} | {'accuracy': 0.8128} |
| 0.0003 | 276.0 | 1932 | 1.3145 | {'f1': 0.8163424124513619} | {'accuracy': 0.8112} |
| 0.0003 | 277.0 | 1939 | 1.3161 | {'f1': 0.8168028004667445} | {'accuracy': 0.8116} |
| 0.0003 | 278.0 | 1946 | 1.3159 | {'f1': 0.8163424124513619} | {'accuracy': 0.8112} |
| 0.0003 | 279.0 | 1953 | 1.3156 | {'f1': 0.8166601790579991} | {'accuracy': 0.8116} |
| 0.0003 | 280.0 | 1960 | 1.3118 | {'f1': 0.8170113148653921} | {'accuracy': 0.8124} |
| 0.0003 | 281.0 | 1967 | 1.3088 | {'f1': 0.8161189358372457} | {'accuracy': 0.812} |
| 0.0003 | 282.0 | 1974 | 1.3077 | {'f1': 0.8145825166601333} | {'accuracy': 0.8108} |
| 0.0003 | 283.0 | 1981 | 1.3072 | {'f1': 0.8149019607843137} | {'accuracy': 0.8112} |
| 0.0003 | 284.0 | 1988 | 1.3075 | {'f1': 0.8149019607843137} | {'accuracy': 0.8112} |
| 0.0003 | 285.0 | 1995 | 1.3084 | {'f1': 0.8149019607843137} | {'accuracy': 0.8112} |
| 0.0001 | 286.0 | 2002 | 1.3097 | {'f1': 0.8147277712495105} | {'accuracy': 0.8108} |
| 0.0001 | 287.0 | 2009 | 1.3106 | {'f1': 0.815655577299413} | {'accuracy': 0.8116} |
| 0.0001 | 288.0 | 2016 | 1.3076 | {'f1': 0.8150765606595994} | {'accuracy': 0.8116} |
| 0.0001 | 289.0 | 2023 | 1.3055 | {'f1': 0.8154269972451791} | {'accuracy': 0.8124} |
| 0.0001 | 290.0 | 2030 | 1.3025 | {'f1': 0.8145224940805051} | {'accuracy': 0.812} |
| 0.0001 | 291.0 | 2037 | 1.3139 | {'f1': 0.8165819319515056} | {'accuracy': 0.8124} |
| 0.0001 | 292.0 | 2044 | 1.3268 | {'f1': 0.8170542635658915} | {'accuracy': 0.8112} |
| 0.0001 | 293.0 | 2051 | 1.3310 | {'f1': 0.8170212765957446} | {'accuracy': 0.8108} |
| 0.0001 | 294.0 | 2058 | 1.3307 | {'f1': 0.8170212765957446} | {'accuracy': 0.8108} |
| 0.0001 | 295.0 | 2065 | 1.4449 | {'f1': 0.8125} | {'accuracy': 0.796} |
| 0.0001 | 296.0 | 2072 | 1.5353 | {'f1': 0.8086175942549373} | {'accuracy': 0.7868} |
| 0.0001 | 297.0 | 2079 | 1.4656 | {'f1': 0.8106530463334549} | {'accuracy': 0.7924} |
| 0.0001 | 298.0 | 2086 | 1.3036 | {'f1': 0.8156028368794326} | {'accuracy': 0.8128} |
| 0.0001 | 299.0 | 2093 | 1.2977 | {'f1': 0.8054410552349547} | {'accuracy': 0.8112} |
| 0.0001 | 300.0 | 2100 | 1.2972 | {'f1': 0.8068739770867429} | {'accuracy': 0.8112} |
| 0.0001 | 301.0 | 2107 | 1.2982 | {'f1': 0.810441767068273} | {'accuracy': 0.8112} |
| 0.0001 | 302.0 | 2114 | 1.3025 | {'f1': 0.8116288331342094} | {'accuracy': 0.8108} |
| 0.0001 | 303.0 | 2121 | 1.3063 | {'f1': 0.8142574257425743} | {'accuracy': 0.8124} |
| 0.0001 | 304.0 | 2128 | 1.3108 | {'f1': 0.8148440584287406} | {'accuracy': 0.8124} |
| 0.0001 | 305.0 | 2135 | 1.3120 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0001 | 306.0 | 2142 | 1.3152 | {'f1': 0.8146399055489965} | {'accuracy': 0.8116} |
| 0.0001 | 307.0 | 2149 | 1.3293 | {'f1': 0.8155339805825242} | {'accuracy': 0.81} |
| 0.0001 | 308.0 | 2156 | 1.3356 | {'f1': 0.8165314793356508} | {'accuracy': 0.81} |
| 0.0001 | 309.0 | 2163 | 1.3352 | {'f1': 0.8163896405102435} | {'accuracy': 0.81} |
| 0.0001 | 310.0 | 2170 | 1.3325 | {'f1': 0.8156771439658517} | {'accuracy': 0.81} |
| 0.0001 | 311.0 | 2177 | 1.3303 | {'f1': 0.815390594636611} | {'accuracy': 0.81} |
| 0.0001 | 312.0 | 2184 | 1.3272 | {'f1': 0.8160561184723305} | {'accuracy': 0.8112} |
| 0.0001 | 313.0 | 2191 | 1.3246 | {'f1': 0.8143806174286832} | {'accuracy': 0.81} |
| 0.0001 | 314.0 | 2198 | 1.3224 | {'f1': 0.8134796238244514} | {'accuracy': 0.8096} |
| 0.0001 | 315.0 | 2205 | 1.3203 | {'f1': 0.815251572327044} | {'accuracy': 0.812} |
| 0.0001 | 316.0 | 2212 | 1.3183 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0001 | 317.0 | 2219 | 1.3132 | {'f1': 0.8129952456418383} | {'accuracy': 0.8112} |
| 0.0001 | 318.0 | 2226 | 1.3111 | {'f1': 0.8127236580516899} | {'accuracy': 0.8116} |
| 0.0001 | 319.0 | 2233 | 1.3078 | {'f1': 0.8101164191087917} | {'accuracy': 0.8108} |
| 0.0001 | 320.0 | 2240 | 1.3076 | {'f1': 0.8096774193548387} | {'accuracy': 0.8112} |
| 0.0001 | 321.0 | 2247 | 1.3090 | {'f1': 0.8101164191087917} | {'accuracy': 0.8108} |
| 0.0001 | 322.0 | 2254 | 1.3433 | {'f1': 0.7892491467576792} | {'accuracy': 0.8024} |
| 0.0001 | 323.0 | 2261 | 1.4595 | {'f1': 0.7642058165548098} | {'accuracy': 0.7892} |
| 0.0001 | 324.0 | 2268 | 1.3247 | {'f1': 0.7968026924694994} | {'accuracy': 0.8068} |
| 0.0001 | 325.0 | 2275 | 1.3326 | {'f1': 0.8177570093457942} | {'accuracy': 0.8128} |
| 0.0001 | 326.0 | 2282 | 1.3992 | {'f1': 0.8167105758374106} | {'accuracy': 0.8052} |
| 0.0001 | 327.0 | 2289 | 1.4017 | {'f1': 0.8177376925967682} | {'accuracy': 0.806} |
| 0.0001 | 328.0 | 2296 | 1.3527 | {'f1': 0.8194070080862534} | {'accuracy': 0.8124} |
| 0.0001 | 329.0 | 2303 | 1.3316 | {'f1': 0.8175465838509317} | {'accuracy': 0.812} |
| 0.0001 | 330.0 | 2310 | 1.3199 | {'f1': 0.8155111633372502} | {'accuracy': 0.8116} |
| 0.0001 | 331.0 | 2317 | 1.3143 | {'f1': 0.8127709893575089} | {'accuracy': 0.81} |
| 0.0001 | 332.0 | 2324 | 1.3109 | {'f1': 0.8113879003558718} | {'accuracy': 0.8092} |
| 0.0001 | 333.0 | 2331 | 1.3092 | {'f1': 0.8114104595879558} | {'accuracy': 0.8096} |
| 0.0001 | 334.0 | 2338 | 1.3085 | {'f1': 0.8104678826328311} | {'accuracy': 0.8088} |
| 0.0001 | 335.0 | 2345 | 1.3083 | {'f1': 0.8107893692978976} | {'accuracy': 0.8092} |
| 0.0001 | 336.0 | 2352 | 1.3086 | {'f1': 0.8107893692978976} | {'accuracy': 0.8092} |
| 0.0001 | 337.0 | 2359 | 1.3096 | {'f1': 0.8109393579072532} | {'accuracy': 0.8092} |
| 0.0001 | 338.0 | 2366 | 1.3108 | {'f1': 0.8118811881188118} | {'accuracy': 0.81} |
| 0.0001 | 339.0 | 2373 | 1.3119 | {'f1': 0.812351543942993} | {'accuracy': 0.8104} |
| 0.0001 | 340.0 | 2380 | 1.3130 | {'f1': 0.8117088607594937} | {'accuracy': 0.8096} |
| 0.0001 | 341.0 | 2387 | 1.3141 | {'f1': 0.8115369419201897} | {'accuracy': 0.8092} |
| 0.0001 | 342.0 | 2394 | 1.3154 | {'f1': 0.811216429699842} | {'accuracy': 0.8088} |
| 0.0001 | 343.0 | 2401 | 1.3151 | {'f1': 0.8115369419201897} | {'accuracy': 0.8092} |
| 0.0001 | 344.0 | 2408 | 1.3154 | {'f1': 0.8115369419201897} | {'accuracy': 0.8092} |
| 0.0001 | 345.0 | 2415 | 1.3156 | {'f1': 0.8115369419201897} | {'accuracy': 0.8092} |
| 0.0001 | 346.0 | 2422 | 1.3157 | {'f1': 0.8115369419201897} | {'accuracy': 0.8092} |
| 0.0001 | 347.0 | 2429 | 1.3158 | {'f1': 0.8115369419201897} | {'accuracy': 0.8092} |
| 0.0001 | 348.0 | 2436 | 1.3338 | {'f1': 0.8160561184723305} | {'accuracy': 0.8112} |
| 0.0001 | 349.0 | 2443 | 1.3439 | {'f1': 0.819062378922898} | {'accuracy': 0.8132} |
| 0.0001 | 350.0 | 2450 | 1.3474 | {'f1': 0.8188854489164088} | {'accuracy': 0.8128} |
| 0.0001 | 351.0 | 2457 | 1.3484 | {'f1': 0.8188854489164088} | {'accuracy': 0.8128} |
| 0.0001 | 352.0 | 2464 | 1.3478 | {'f1': 0.8188854489164088} | {'accuracy': 0.8128} |
| 0.0001 | 353.0 | 2471 | 1.3462 | {'f1': 0.8186046511627906} | {'accuracy': 0.8128} |
| 0.0001 | 354.0 | 2478 | 1.3432 | {'f1': 0.8183229813664596} | {'accuracy': 0.8128} |
| 0.0001 | 355.0 | 2485 | 1.3415 | {'f1': 0.8172628304821151} | {'accuracy': 0.812} |
| 0.0001 | 356.0 | 2492 | 1.3380 | {'f1': 0.8166601790579991} | {'accuracy': 0.8116} |
| 0.0001 | 357.0 | 2499 | 1.3354 | {'f1': 0.8165495706479313} | {'accuracy': 0.812} |
| 0.0011 | 358.0 | 2506 | 1.3370 | {'f1': 0.816374269005848} | {'accuracy': 0.8116} |
| 0.0011 | 359.0 | 2513 | 1.3384 | {'f1': 0.8172964550058435} | {'accuracy': 0.8124} |
| 0.0011 | 360.0 | 2520 | 1.3373 | {'f1': 0.8166926677067083} | {'accuracy': 0.812} |
| 0.0011 | 361.0 | 2527 | 1.3354 | {'f1': 0.8157689305230289} | {'accuracy': 0.8112} |
| 0.0011 | 362.0 | 2534 | 1.3336 | {'f1': 0.8153364632237872} | {'accuracy': 0.8112} |
| 0.0011 | 363.0 | 2541 | 1.3321 | {'f1': 0.8145825166601333} | {'accuracy': 0.8108} |
| 0.0011 | 364.0 | 2548 | 1.3280 | {'f1': 0.8149312377210216} | {'accuracy': 0.8116} |
| 0.0011 | 365.0 | 2555 | 1.3711 | {'f1': 0.819433817903596} | {'accuracy': 0.8112} |
| 0.0011 | 366.0 | 2562 | 1.4276 | {'f1': 0.8177083333333331} | {'accuracy': 0.804} |
| 0.0011 | 367.0 | 2569 | 1.4536 | {'f1': 0.8159645232815964} | {'accuracy': 0.8008} |
| 0.0011 | 368.0 | 2576 | 1.4590 | {'f1': 0.8161004431314622} | {'accuracy': 0.8008} |
| 0.0011 | 369.0 | 2583 | 1.3146 | {'f1': 0.8145224940805051} | {'accuracy': 0.812} |
| 0.0011 | 370.0 | 2590 | 1.3096 | {'f1': 0.8057851239669422} | {'accuracy': 0.812} |
| 0.0011 | 371.0 | 2597 | 1.3042 | {'f1': 0.8077080770807707} | {'accuracy': 0.8124} |
| 0.0011 | 372.0 | 2604 | 1.3011 | {'f1': 0.8080065359477124} | {'accuracy': 0.812} |
| 0.0011 | 373.0 | 2611 | 1.3000 | {'f1': 0.8090982940698618} | {'accuracy': 0.812} |
| 0.0011 | 374.0 | 2618 | 1.3001 | {'f1': 0.8127522195318806} | {'accuracy': 0.8144} |
| 0.0011 | 375.0 | 2625 | 1.3009 | {'f1': 0.8102893890675241} | {'accuracy': 0.8112} |
| 0.0011 | 376.0 | 2632 | 1.3019 | {'f1': 0.8102687525070197} | {'accuracy': 0.8108} |
| 0.0011 | 377.0 | 2639 | 1.3028 | {'f1': 0.8112} | {'accuracy': 0.8112} |
| 0.0011 | 378.0 | 2646 | 1.3038 | {'f1': 0.8119760479041915} | {'accuracy': 0.8116} |
| 0.0011 | 379.0 | 2653 | 1.3058 | {'f1': 0.8125746120175089} | {'accuracy': 0.8116} |
| 0.0011 | 380.0 | 2660 | 1.3096 | {'f1': 0.8134653465346535} | {'accuracy': 0.8116} |
| 0.0011 | 381.0 | 2667 | 1.3122 | {'f1': 0.8164232135807343} | {'accuracy': 0.814} |
| 0.0011 | 382.0 | 2674 | 1.3137 | {'f1': 0.8168902920284135} | {'accuracy': 0.8144} |
| 0.0011 | 383.0 | 2681 | 1.3156 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 384.0 | 2688 | 1.3162 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 385.0 | 2695 | 1.3165 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 386.0 | 2702 | 1.3168 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 387.0 | 2709 | 1.3169 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 388.0 | 2716 | 1.3166 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 389.0 | 2723 | 1.3166 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 390.0 | 2730 | 1.3168 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 391.0 | 2737 | 1.3165 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 392.0 | 2744 | 1.3168 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 393.0 | 2751 | 1.3172 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 394.0 | 2758 | 1.3173 | {'f1': 0.8170347003154574} | {'accuracy': 0.8144} |
| 0.0011 | 395.0 | 2765 | 1.3161 | {'f1': 0.8154879494271038} | {'accuracy': 0.8132} |
| 0.0011 | 396.0 | 2772 | 1.3156 | {'f1': 0.8148734177215189} | {'accuracy': 0.8128} |
| 0.0011 | 397.0 | 2779 | 1.3148 | {'f1': 0.8129952456418383} | {'accuracy': 0.8112} |
| 0.0011 | 398.0 | 2786 | 1.3146 | {'f1': 0.8129952456418383} | {'accuracy': 0.8112} |
| 0.0011 | 399.0 | 2793 | 1.3142 | {'f1': 0.8133174791914388} | {'accuracy': 0.8116} |
| 0.0011 | 400.0 | 2800 | 1.3146 | {'f1': 0.8129952456418383} | {'accuracy': 0.8112} |
| 0.0011 | 401.0 | 2807 | 1.3163 | {'f1': 0.8139350752177354} | {'accuracy': 0.812} |
| 0.0011 | 402.0 | 2814 | 1.3147 | {'f1': 0.8139627132090439} | {'accuracy': 0.8124} |
| 0.0011 | 403.0 | 2821 | 1.3137 | {'f1': 0.813195548489666} | {'accuracy': 0.812} |
| 0.0011 | 404.0 | 2828 | 1.3133 | {'f1': 0.8135188866799204} | {'accuracy': 0.8124} |
| 0.0011 | 405.0 | 2835 | 1.3132 | {'f1': 0.8135188866799204} | {'accuracy': 0.8124} |
| 0.0011 | 406.0 | 2842 | 1.3132 | {'f1': 0.8125746120175089} | {'accuracy': 0.8116} |
| 0.0011 | 407.0 | 2849 | 1.3132 | {'f1': 0.8121019108280254} | {'accuracy': 0.8112} |
| 0.0011 | 408.0 | 2856 | 1.3146 | {'f1': 0.8130469371519491} | {'accuracy': 0.812} |
| 0.0011 | 409.0 | 2863 | 1.3186 | {'f1': 0.8144044321329641} | {'accuracy': 0.8124} |
| 0.0011 | 410.0 | 2870 | 1.3217 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0011 | 411.0 | 2877 | 1.3233 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 412.0 | 2884 | 1.3243 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 413.0 | 2891 | 1.3248 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 414.0 | 2898 | 1.3249 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 415.0 | 2905 | 1.3248 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 416.0 | 2912 | 1.3249 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 417.0 | 2919 | 1.3251 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 418.0 | 2926 | 1.3250 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 419.0 | 2933 | 1.3250 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 420.0 | 2940 | 1.3250 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 421.0 | 2947 | 1.3246 | {'f1': 0.8162460567823343} | {'accuracy': 0.8136} |
| 0.0011 | 422.0 | 2954 | 1.3244 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0011 | 423.0 | 2961 | 1.3242 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0011 | 424.0 | 2968 | 1.3245 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0011 | 425.0 | 2975 | 1.3256 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 426.0 | 2982 | 1.3260 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 427.0 | 2989 | 1.3261 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0011 | 428.0 | 2996 | 1.3264 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 429.0 | 3003 | 1.3265 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 430.0 | 3010 | 1.3268 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 431.0 | 3017 | 1.3265 | {'f1': 0.8162460567823343} | {'accuracy': 0.8136} |
| 0.0 | 432.0 | 3024 | 1.3260 | {'f1': 0.8161010260457774} | {'accuracy': 0.8136} |
| 0.0 | 433.0 | 3031 | 1.3259 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 434.0 | 3038 | 1.3260 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 435.0 | 3045 | 1.3262 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 436.0 | 3052 | 1.3257 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 437.0 | 3059 | 1.3255 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 438.0 | 3066 | 1.3250 | {'f1': 0.8154879494271038} | {'accuracy': 0.8132} |
| 0.0 | 439.0 | 3073 | 1.3247 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0 | 440.0 | 3080 | 1.3245 | {'f1': 0.8144044321329641} | {'accuracy': 0.8124} |
| 0.0 | 441.0 | 3087 | 1.3242 | {'f1': 0.8144044321329641} | {'accuracy': 0.8124} |
| 0.0 | 442.0 | 3094 | 1.3243 | {'f1': 0.8144044321329641} | {'accuracy': 0.8124} |
| 0.0 | 443.0 | 3101 | 1.3247 | {'f1': 0.8144044321329641} | {'accuracy': 0.8124} |
| 0.0 | 444.0 | 3108 | 1.3250 | {'f1': 0.8144044321329641} | {'accuracy': 0.8124} |
| 0.0 | 445.0 | 3115 | 1.3254 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0 | 446.0 | 3122 | 1.3254 | {'f1': 0.8148734177215189} | {'accuracy': 0.8128} |
| 0.0 | 447.0 | 3129 | 1.3257 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0 | 448.0 | 3136 | 1.3258 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0 | 449.0 | 3143 | 1.3260 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0 | 450.0 | 3150 | 1.3264 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0 | 451.0 | 3157 | 1.3270 | {'f1': 0.815955766192733} | {'accuracy': 0.8136} |
| 0.0 | 452.0 | 3164 | 1.3273 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 453.0 | 3171 | 1.3276 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 454.0 | 3178 | 1.3277 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 455.0 | 3185 | 1.3278 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 456.0 | 3192 | 1.3279 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 457.0 | 3199 | 1.3283 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 458.0 | 3206 | 1.3285 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 459.0 | 3213 | 1.3288 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 460.0 | 3220 | 1.3290 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 461.0 | 3227 | 1.3291 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 462.0 | 3234 | 1.3291 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 463.0 | 3241 | 1.3296 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0 | 464.0 | 3248 | 1.3298 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0 | 465.0 | 3255 | 1.3297 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0 | 466.0 | 3262 | 1.3295 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 467.0 | 3269 | 1.3298 | {'f1': 0.8156336360047375} | {'accuracy': 0.8132} |
| 0.0 | 468.0 | 3276 | 1.3301 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0 | 469.0 | 3283 | 1.3306 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0 | 470.0 | 3290 | 1.3309 | {'f1': 0.8153117600631413} | {'accuracy': 0.8128} |
| 0.0 | 471.0 | 3297 | 1.3321 | {'f1': 0.8162460567823343} | {'accuracy': 0.8136} |
| 0.0 | 472.0 | 3304 | 1.3328 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 473.0 | 3311 | 1.3333 | {'f1': 0.8176447420244192} | {'accuracy': 0.8148} |
| 0.0 | 474.0 | 3318 | 1.3335 | {'f1': 0.8176447420244192} | {'accuracy': 0.8148} |
| 0.0 | 475.0 | 3325 | 1.3335 | {'f1': 0.8176447420244192} | {'accuracy': 0.8148} |
| 0.0 | 476.0 | 3332 | 1.3336 | {'f1': 0.8176447420244192} | {'accuracy': 0.8148} |
| 0.0 | 477.0 | 3339 | 1.3336 | {'f1': 0.8176447420244192} | {'accuracy': 0.8148} |
| 0.0 | 478.0 | 3346 | 1.3337 | {'f1': 0.8176447420244192} | {'accuracy': 0.8148} |
| 0.0 | 479.0 | 3353 | 1.3335 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 480.0 | 3360 | 1.3334 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 481.0 | 3367 | 1.3336 | {'f1': 0.8167126527394561} | {'accuracy': 0.814} |
| 0.0 | 482.0 | 3374 | 1.3336 | {'f1': 0.8171788810086682} | {'accuracy': 0.8144} |
| 0.0 | 483.0 | 3381 | 1.3534 | {'f1': 0.8176538908246225} | {'accuracy': 0.8116} |
| 0.0 | 484.0 | 3388 | 1.3670 | {'f1': 0.8195836545875097} | {'accuracy': 0.8128} |
| 0.0 | 485.0 | 3395 | 1.3735 | {'f1': 0.8201383551114528} | {'accuracy': 0.8128} |
| 0.0 | 486.0 | 3402 | 1.3764 | {'f1': 0.8216340621403913} | {'accuracy': 0.814} |
| 0.0 | 487.0 | 3409 | 1.3759 | {'f1': 0.8216340621403913} | {'accuracy': 0.814} |
| 0.0 | 488.0 | 3416 | 1.3750 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 489.0 | 3423 | 1.3743 | {'f1': 0.8207293666026871} | {'accuracy': 0.8132} |
| 0.0 | 490.0 | 3430 | 1.3739 | {'f1': 0.8207293666026871} | {'accuracy': 0.8132} |
| 0.0 | 491.0 | 3437 | 1.3746 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 492.0 | 3444 | 1.3754 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 493.0 | 3451 | 1.3755 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 494.0 | 3458 | 1.3754 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 495.0 | 3465 | 1.3753 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 496.0 | 3472 | 1.3751 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 497.0 | 3479 | 1.3749 | {'f1': 0.8211818879508825} | {'accuracy': 0.8136} |
| 0.0 | 498.0 | 3486 | 1.3746 | {'f1': 0.8207293666026871} | {'accuracy': 0.8132} |
| 0.0 | 499.0 | 3493 | 1.3743 | {'f1': 0.8207293666026871} | {'accuracy': 0.8132} |
| 0.0 | 500.0 | 3500 | 1.3742 | {'f1': 0.8207293666026871} | {'accuracy': 0.8132} |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
