<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
bert-base-uncased-test_16_200
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: 1.4523
- F1: {'f1': 0.8282041293338528}
- Accuracy: {'accuracy': 0.8236}
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 | 13 | 0.6924 | {'f1': 0.34501642935377874} | {'accuracy': 0.5216} |
| No log | 2.0 | 26 | 0.6882 | {'f1': 0.46713947990543736} | {'accuracy': 0.5492} |
| No log | 3.0 | 39 | 0.6838 | {'f1': 0.5512547851977881} | {'accuracy': 0.578} |
| No log | 4.0 | 52 | 0.6769 | {'f1': 0.42633567188343224} | {'accuracy': 0.5748} |
| No log | 5.0 | 65 | 0.6556 | {'f1': 0.5819964349376114} | {'accuracy': 0.6248} |
| No log | 6.0 | 78 | 0.6259 | {'f1': 0.6498553121124432} | {'accuracy': 0.6612} |
| No log | 7.0 | 91 | 0.5996 | {'f1': 0.6816693944353519} | {'accuracy': 0.6888} |
| No log | 8.0 | 104 | 0.5652 | {'f1': 0.7300184162062615} | {'accuracy': 0.7068} |
| No log | 9.0 | 117 | 0.5545 | {'f1': 0.7577464788732394} | {'accuracy': 0.7248} |
| No log | 10.0 | 130 | 0.5519 | {'f1': 0.7614510809820447} | {'accuracy': 0.7396} |
| No log | 11.0 | 143 | 0.5610 | {'f1': 0.7769886363636364} | {'accuracy': 0.7488} |
| No log | 12.0 | 156 | 0.5165 | {'f1': 0.7841921735761334} | {'accuracy': 0.7772} |
| No log | 13.0 | 169 | 0.5503 | {'f1': 0.7601539119281746} | {'accuracy': 0.7756} |
| No log | 14.0 | 182 | 0.5292 | {'f1': 0.7821740941274469} | {'accuracy': 0.7908} |
| No log | 15.0 | 195 | 0.5220 | {'f1': 0.8040238450074516} | {'accuracy': 0.7896} |
| No log | 16.0 | 208 | 0.5622 | {'f1': 0.8059701492537312} | {'accuracy': 0.8024} |
| No log | 17.0 | 221 | 0.5942 | {'f1': 0.8030182684670374} | {'accuracy': 0.8016} |
| No log | 18.0 | 234 | 0.6197 | {'f1': 0.8131952435749904} | {'accuracy': 0.8052} |
| No log | 19.0 | 247 | 0.6677 | {'f1': 0.8140060240963856} | {'accuracy': 0.8024} |
| No log | 20.0 | 260 | 0.7322 | {'f1': 0.7898305084745764} | {'accuracy': 0.8016} |
| No log | 21.0 | 273 | 0.7278 | {'f1': 0.8039457459926018} | {'accuracy': 0.8092} |
| No log | 22.0 | 286 | 0.7253 | {'f1': 0.8110419906687402} | {'accuracy': 0.8056} |
| No log | 23.0 | 299 | 0.7409 | {'f1': 0.8124507486209613} | {'accuracy': 0.8096} |
| No log | 24.0 | 312 | 0.7735 | {'f1': 0.8232169954476479} | {'accuracy': 0.8136} |
| No log | 25.0 | 325 | 0.7893 | {'f1': 0.8152969894222944} | {'accuracy': 0.8184} |
| No log | 26.0 | 338 | 0.8000 | {'f1': 0.817246835443038} | {'accuracy': 0.8152} |
| No log | 27.0 | 351 | 0.8183 | {'f1': 0.8175009854158455} | {'accuracy': 0.8148} |
| No log | 28.0 | 364 | 0.8288 | {'f1': 0.8190776507686244} | {'accuracy': 0.8164} |
| No log | 29.0 | 377 | 0.8793 | {'f1': 0.8233124308373294} | {'accuracy': 0.8084} |
| No log | 30.0 | 390 | 0.8778 | {'f1': 0.8087206910736323} | {'accuracy': 0.814} |
| No log | 31.0 | 403 | 0.8484 | {'f1': 0.824655436447167} | {'accuracy': 0.8168} |
| No log | 32.0 | 416 | 0.8732 | {'f1': 0.82834406972338} | {'accuracy': 0.8188} |
| No log | 33.0 | 429 | 0.8856 | {'f1': 0.8159362549800797} | {'accuracy': 0.8152} |
| No log | 34.0 | 442 | 0.9012 | {'f1': 0.8258859784283513} | {'accuracy': 0.8192} |
| No log | 35.0 | 455 | 0.9184 | {'f1': 0.8156645569620253} | {'accuracy': 0.8136} |
| No log | 36.0 | 468 | 0.9545 | {'f1': 0.823352165725047} | {'accuracy': 0.8124} |
| No log | 37.0 | 481 | 0.9520 | {'f1': 0.8212074303405573} | {'accuracy': 0.8152} |
| No log | 38.0 | 494 | 0.9724 | {'f1': 0.81181707810603} | {'accuracy': 0.814} |
| 0.1904 | 39.0 | 507 | 0.9668 | {'f1': 0.8222648752399232} | {'accuracy': 0.8148} |
| 0.1904 | 40.0 | 520 | 0.9684 | {'f1': 0.8177813376051262} | {'accuracy': 0.818} |
| 0.1904 | 41.0 | 533 | 0.9888 | {'f1': 0.8287878787878789} | {'accuracy': 0.8192} |
| 0.1904 | 42.0 | 546 | 1.0402 | {'f1': 0.8071636817992504} | {'accuracy': 0.8148} |
| 0.1904 | 43.0 | 559 | 1.0224 | {'f1': 0.818640776699029} | {'accuracy': 0.8132} |
| 0.1904 | 44.0 | 572 | 1.0390 | {'f1': 0.817864077669903} | {'accuracy': 0.8124} |
| 0.1904 | 45.0 | 585 | 1.1882 | {'f1': 0.7851851851851853} | {'accuracy': 0.8028} |
| 0.1904 | 46.0 | 598 | 1.0246 | {'f1': 0.8259860788863109} | {'accuracy': 0.82} |
| 0.1904 | 47.0 | 611 | 1.0492 | {'f1': 0.8100535640708695} | {'accuracy': 0.8156} |
| 0.1904 | 48.0 | 624 | 1.0127 | {'f1': 0.8293448411141624} | {'accuracy': 0.826} |
| 0.1904 | 49.0 | 637 | 1.1182 | {'f1': 0.8010247651579845} | {'accuracy': 0.8136} |
| 0.1904 | 50.0 | 650 | 1.0229 | {'f1': 0.8275049115913556} | {'accuracy': 0.8244} |
| 0.1904 | 51.0 | 663 | 1.0610 | {'f1': 0.8301026225769669} | {'accuracy': 0.8212} |
| 0.1904 | 52.0 | 676 | 1.0652 | {'f1': 0.8152350081037277} | {'accuracy': 0.8176} |
| 0.1904 | 53.0 | 689 | 1.0522 | {'f1': 0.825545171339564} | {'accuracy': 0.8208} |
| 0.1904 | 54.0 | 702 | 1.0731 | {'f1': 0.8177813376051262} | {'accuracy': 0.818} |
| 0.1904 | 55.0 | 715 | 1.0828 | {'f1': 0.8180379746835443} | {'accuracy': 0.816} |
| 0.1904 | 56.0 | 728 | 1.0955 | {'f1': 0.8228483211115398} | {'accuracy': 0.8164} |
| 0.1904 | 57.0 | 741 | 1.1408 | {'f1': 0.8041666666666666} | {'accuracy': 0.812} |
| 0.1904 | 58.0 | 754 | 1.1443 | {'f1': 0.8273062730627306} | {'accuracy': 0.8128} |
| 0.1904 | 59.0 | 767 | 1.1490 | {'f1': 0.8041407867494824} | {'accuracy': 0.8108} |
| 0.1904 | 60.0 | 780 | 1.1251 | {'f1': 0.8243700639338096} | {'accuracy': 0.8132} |
| 0.1904 | 61.0 | 793 | 1.1560 | {'f1': 0.8067993366500829} | {'accuracy': 0.8136} |
| 0.1904 | 62.0 | 806 | 1.1154 | {'f1': 0.8140544808527438} | {'accuracy': 0.8116} |
| 0.1904 | 63.0 | 819 | 1.1284 | {'f1': 0.8237547892720306} | {'accuracy': 0.816} |
| 0.1904 | 64.0 | 832 | 1.1035 | {'f1': 0.8199052132701422} | {'accuracy': 0.8176} |
| 0.1904 | 65.0 | 845 | 1.1498 | {'f1': 0.8122162608336772} | {'accuracy': 0.818} |
| 0.1904 | 66.0 | 858 | 1.1032 | {'f1': 0.824313725490196} | {'accuracy': 0.8208} |
| 0.1904 | 67.0 | 871 | 1.1103 | {'f1': 0.8270270270270269} | {'accuracy': 0.8208} |
| 0.1904 | 68.0 | 884 | 1.1100 | {'f1': 0.8249027237354084} | {'accuracy': 0.82} |
| 0.1904 | 69.0 | 897 | 1.1118 | {'f1': 0.8255950058525166} | {'accuracy': 0.8212} |
| 0.1904 | 70.0 | 910 | 1.1415 | {'f1': 0.8293963254593175} | {'accuracy': 0.818} |
| 0.1904 | 71.0 | 923 | 1.1696 | {'f1': 0.8101582014987511} | {'accuracy': 0.8176} |
| 0.1904 | 72.0 | 936 | 1.1047 | {'f1': 0.8335255670895808} | {'accuracy': 0.8268} |
| 0.1904 | 73.0 | 949 | 1.1170 | {'f1': 0.8350983358547655} | {'accuracy': 0.8256} |
| 0.1904 | 74.0 | 962 | 1.0945 | {'f1': 0.8333333333333333} | {'accuracy': 0.828} |
| 0.1904 | 75.0 | 975 | 1.1270 | {'f1': 0.8357760240511086} | {'accuracy': 0.8252} |
| 0.1904 | 76.0 | 988 | 1.1019 | {'f1': 0.8337832626301581} | {'accuracy': 0.8276} |
| 0.0034 | 77.0 | 1001 | 1.1019 | {'f1': 0.8329448329448329} | {'accuracy': 0.828} |
| 0.0034 | 78.0 | 1014 | 1.1066 | {'f1': 0.8326213592233009} | {'accuracy': 0.8276} |
| 0.0034 | 79.0 | 1027 | 1.1238 | {'f1': 0.8217741935483871} | {'accuracy': 0.8232} |
| 0.0034 | 80.0 | 1040 | 1.1272 | {'f1': 0.820823244552058} | {'accuracy': 0.8224} |
| 0.0034 | 81.0 | 1053 | 1.1152 | {'f1': 0.8322304398598677} | {'accuracy': 0.8276} |
| 0.0034 | 82.0 | 1066 | 1.1221 | {'f1': 0.8333974605617546} | {'accuracy': 0.8268} |
| 0.0034 | 83.0 | 1079 | 1.1257 | {'f1': 0.834355828220859} | {'accuracy': 0.8272} |
| 0.0034 | 84.0 | 1092 | 1.1243 | {'f1': 0.8333974605617546} | {'accuracy': 0.8268} |
| 0.0034 | 85.0 | 1105 | 1.1229 | {'f1': 0.8332046332046331} | {'accuracy': 0.8272} |
| 0.0034 | 86.0 | 1118 | 1.1460 | {'f1': 0.8208045509955302} | {'accuracy': 0.8236} |
| 0.0034 | 87.0 | 1131 | 1.1643 | {'f1': 0.8331450094161958} | {'accuracy': 0.8228} |
| 0.0034 | 88.0 | 1144 | 1.1578 | {'f1': 0.825090470446321} | {'accuracy': 0.826} |
| 0.0034 | 89.0 | 1157 | 1.3734 | {'f1': 0.8269774011299434} | {'accuracy': 0.804} |
| 0.0034 | 90.0 | 1170 | 1.3068 | {'f1': 0.7988013698630138} | {'accuracy': 0.812} |
| 0.0034 | 91.0 | 1183 | 1.2197 | {'f1': 0.8332714444857038} | {'accuracy': 0.8204} |
| 0.0034 | 92.0 | 1196 | 1.1748 | {'f1': 0.8174190970834998} | {'accuracy': 0.8172} |
| 0.0034 | 93.0 | 1209 | 1.1761 | {'f1': 0.8166062071745264} | {'accuracy': 0.818} |
| 0.0034 | 94.0 | 1222 | 1.2006 | {'f1': 0.8146622734761121} | {'accuracy': 0.82} |
| 0.0034 | 95.0 | 1235 | 1.1610 | {'f1': 0.8323076923076923} | {'accuracy': 0.8256} |
| 0.0034 | 96.0 | 1248 | 1.1756 | {'f1': 0.8370510396975426} | {'accuracy': 0.8276} |
| 0.0034 | 97.0 | 1261 | 1.2171 | {'f1': 0.812603648424544} | {'accuracy': 0.8192} |
| 0.0034 | 98.0 | 1274 | 1.1959 | {'f1': 0.8168783285538713} | {'accuracy': 0.8212} |
| 0.0034 | 99.0 | 1287 | 1.1805 | {'f1': 0.8344774980930587} | {'accuracy': 0.8264} |
| 0.0034 | 100.0 | 1300 | 1.1942 | {'f1': 0.8371741594257649} | {'accuracy': 0.8276} |
| 0.0034 | 101.0 | 1313 | 1.1747 | {'f1': 0.8332694518972786} | {'accuracy': 0.826} |
| 0.0034 | 102.0 | 1326 | 1.1655 | {'f1': 0.8292301680343885} | {'accuracy': 0.8252} |
| 0.0034 | 103.0 | 1339 | 1.1666 | {'f1': 0.8296875} | {'accuracy': 0.8256} |
| 0.0034 | 104.0 | 1352 | 1.1706 | {'f1': 0.8340425531914895} | {'accuracy': 0.8284} |
| 0.0034 | 105.0 | 1365 | 1.2513 | {'f1': 0.808421052631579} | {'accuracy': 0.818} |
| 0.0034 | 106.0 | 1378 | 1.1799 | {'f1': 0.8342989571263036} | {'accuracy': 0.8284} |
| 0.0034 | 107.0 | 1391 | 1.1937 | {'f1': 0.8216303470540758} | {'accuracy': 0.8232} |
| 0.0034 | 108.0 | 1404 | 1.2025 | {'f1': 0.82414068745004} | {'accuracy': 0.824} |
| 0.0034 | 109.0 | 1417 | 1.2674 | {'f1': 0.8359866716031099} | {'accuracy': 0.8228} |
| 0.0034 | 110.0 | 1430 | 1.2135 | {'f1': 0.8231587239070499} | {'accuracy': 0.8204} |
| 0.0034 | 111.0 | 1443 | 1.2182 | {'f1': 0.821656050955414} | {'accuracy': 0.8208} |
| 0.0034 | 112.0 | 1456 | 1.2181 | {'f1': 0.8222664015904573} | {'accuracy': 0.8212} |
| 0.0034 | 113.0 | 1469 | 1.2174 | {'f1': 0.823156225218081} | {'accuracy': 0.8216} |
| 0.0034 | 114.0 | 1482 | 1.2166 | {'f1': 0.825296442687747} | {'accuracy': 0.8232} |
| 0.0034 | 115.0 | 1495 | 1.2167 | {'f1': 0.8249118683901293} | {'accuracy': 0.8212} |
| 0.002 | 116.0 | 1508 | 1.2176 | {'f1': 0.8253223915592028} | {'accuracy': 0.8212} |
| 0.002 | 117.0 | 1521 | 1.3367 | {'f1': 0.7998289136013687} | {'accuracy': 0.8128} |
| 0.002 | 118.0 | 1534 | 1.4711 | {'f1': 0.8263009845288326} | {'accuracy': 0.8024} |
| 0.002 | 119.0 | 1547 | 1.3399 | {'f1': 0.8018628281117698} | {'accuracy': 0.8128} |
| 0.002 | 120.0 | 1560 | 1.3563 | {'f1': 0.7993197278911565} | {'accuracy': 0.8112} |
| 0.002 | 121.0 | 1573 | 1.2461 | {'f1': 0.8310888803385917} | {'accuracy': 0.8244} |
| 0.002 | 122.0 | 1586 | 1.2615 | {'f1': 0.8359788359788359} | {'accuracy': 0.8264} |
| 0.002 | 123.0 | 1599 | 1.3245 | {'f1': 0.8064107971320118} | {'accuracy': 0.8164} |
| 0.002 | 124.0 | 1612 | 1.2978 | {'f1': 0.8384473197781885} | {'accuracy': 0.8252} |
| 0.002 | 125.0 | 1625 | 1.2612 | {'f1': 0.8151465798045603} | {'accuracy': 0.8184} |
| 0.002 | 126.0 | 1638 | 1.2248 | {'f1': 0.8311890838206627} | {'accuracy': 0.8268} |
| 0.002 | 127.0 | 1651 | 1.2302 | {'f1': 0.8400609291698401} | {'accuracy': 0.832} |
| 0.002 | 128.0 | 1664 | 1.2314 | {'f1': 0.8391023202738684} | {'accuracy': 0.8308} |
| 0.002 | 129.0 | 1677 | 1.2311 | {'f1': 0.8397411496003045} | {'accuracy': 0.8316} |
| 0.002 | 130.0 | 1690 | 1.2292 | {'f1': 0.8390541571319603} | {'accuracy': 0.8312} |
| 0.002 | 131.0 | 1703 | 1.2272 | {'f1': 0.8374233128834357} | {'accuracy': 0.8304} |
| 0.002 | 132.0 | 1716 | 1.2271 | {'f1': 0.8374951978486362} | {'accuracy': 0.8308} |
| 0.002 | 133.0 | 1729 | 1.2278 | {'f1': 0.8379416282642089} | {'accuracy': 0.8312} |
| 0.002 | 134.0 | 1742 | 1.2255 | {'f1': 0.8327512611563834} | {'accuracy': 0.8276} |
| 0.002 | 135.0 | 1755 | 1.2323 | {'f1': 0.8382409177820267} | {'accuracy': 0.8308} |
| 0.002 | 136.0 | 1768 | 1.2486 | {'f1': 0.8400000000000001} | {'accuracy': 0.8304} |
| 0.002 | 137.0 | 1781 | 1.2466 | {'f1': 0.8393194706994328} | {'accuracy': 0.83} |
| 0.002 | 138.0 | 1794 | 1.2443 | {'f1': 0.8389057750759878} | {'accuracy': 0.8304} |
| 0.002 | 139.0 | 1807 | 1.2382 | {'f1': 0.8370313695485846} | {'accuracy': 0.8296} |
| 0.002 | 140.0 | 1820 | 1.2361 | {'f1': 0.8357748650732459} | {'accuracy': 0.8296} |
| 0.002 | 141.0 | 1833 | 1.2361 | {'f1': 0.8338485316846985} | {'accuracy': 0.828} |
| 0.002 | 142.0 | 1846 | 1.2370 | {'f1': 0.8347490347490347} | {'accuracy': 0.8288} |
| 0.002 | 143.0 | 1859 | 1.2379 | {'f1': 0.8347490347490347} | {'accuracy': 0.8288} |
| 0.002 | 144.0 | 1872 | 1.2389 | {'f1': 0.8353258773621287} | {'accuracy': 0.8292} |
| 0.002 | 145.0 | 1885 | 1.2399 | {'f1': 0.8363496341932999} | {'accuracy': 0.83} |
| 0.002 | 146.0 | 1898 | 1.2405 | {'f1': 0.8370484242890085} | {'accuracy': 0.8304} |
| 0.002 | 147.0 | 1911 | 1.2413 | {'f1': 0.8380660015349194} | {'accuracy': 0.8312} |
| 0.002 | 148.0 | 1924 | 1.2428 | {'f1': 0.837796480489671} | {'accuracy': 0.8304} |
| 0.002 | 149.0 | 1937 | 1.2422 | {'f1': 0.836153846153846} | {'accuracy': 0.8296} |
| 0.002 | 150.0 | 1950 | 1.2429 | {'f1': 0.8342989571263036} | {'accuracy': 0.8284} |
| 0.002 | 151.0 | 1963 | 1.2446 | {'f1': 0.8338485316846985} | {'accuracy': 0.828} |
| 0.002 | 152.0 | 1976 | 1.2456 | {'f1': 0.8338485316846985} | {'accuracy': 0.828} |
| 0.002 | 153.0 | 1989 | 1.2492 | {'f1': 0.8294786358290867} | {'accuracy': 0.826} |
| 0.0006 | 154.0 | 2002 | 1.2568 | {'f1': 0.8233425962683605} | {'accuracy': 0.822} |
| 0.0006 | 155.0 | 2015 | 1.2544 | {'f1': 0.8274772996446901} | {'accuracy': 0.8252} |
| 0.0006 | 156.0 | 2028 | 1.2590 | {'f1': 0.821841371064169} | {'accuracy': 0.8212} |
| 0.0006 | 157.0 | 2041 | 1.2627 | {'f1': 0.8203252032520325} | {'accuracy': 0.8232} |
| 0.0006 | 158.0 | 2054 | 1.2343 | {'f1': 0.8357748650732459} | {'accuracy': 0.8296} |
| 0.0006 | 159.0 | 2067 | 1.2512 | {'f1': 0.8401515151515151} | {'accuracy': 0.8312} |
| 0.0006 | 160.0 | 2080 | 1.2342 | {'f1': 0.8332679482149863} | {'accuracy': 0.83} |
| 0.0006 | 161.0 | 2093 | 1.2540 | {'f1': 0.8236245954692557} | {'accuracy': 0.8256} |
| 0.0006 | 162.0 | 2106 | 1.2716 | {'f1': 0.8407212622088654} | {'accuracy': 0.8304} |
| 0.0006 | 163.0 | 2119 | 1.3067 | {'f1': 0.8432752871433864} | {'accuracy': 0.8308} |
| 0.0006 | 164.0 | 2132 | 1.3170 | {'f1': 0.8439690151235706} | {'accuracy': 0.8308} |
| 0.0006 | 165.0 | 2145 | 1.2534 | {'f1': 0.8348765432098765} | {'accuracy': 0.8288} |
| 0.0006 | 166.0 | 2158 | 1.4568 | {'f1': 0.8335724533715927} | {'accuracy': 0.8144} |
| 0.0006 | 167.0 | 2171 | 1.7255 | {'f1': 0.7619485294117648} | {'accuracy': 0.7928} |
| 0.0006 | 168.0 | 2184 | 1.3573 | {'f1': 0.8297715549005158} | {'accuracy': 0.8152} |
| 0.0006 | 169.0 | 2197 | 1.3207 | {'f1': 0.8359433258762118} | {'accuracy': 0.824} |
| 0.0006 | 170.0 | 2210 | 1.3038 | {'f1': 0.8335843373493976} | {'accuracy': 0.8232} |
| 0.0006 | 171.0 | 2223 | 1.2915 | {'f1': 0.8342205323193916} | {'accuracy': 0.8256} |
| 0.0006 | 172.0 | 2236 | 1.2849 | {'f1': 0.8298117556665386} | {'accuracy': 0.8228} |
| 0.0006 | 173.0 | 2249 | 1.4631 | {'f1': 0.8022212729602733} | {'accuracy': 0.8148} |
| 0.0006 | 174.0 | 2262 | 1.3428 | {'f1': 0.8366592756836658} | {'accuracy': 0.8232} |
| 0.0006 | 175.0 | 2275 | 1.2966 | {'f1': 0.8220472440944883} | {'accuracy': 0.8192} |
| 0.0006 | 176.0 | 2288 | 1.3188 | {'f1': 0.8358662613981763} | {'accuracy': 0.8272} |
| 0.0006 | 177.0 | 2301 | 1.4370 | {'f1': 0.8347826086956524} | {'accuracy': 0.8176} |
| 0.0006 | 178.0 | 2314 | 1.3209 | {'f1': 0.8352985926207683} | {'accuracy': 0.8268} |
| 0.0006 | 179.0 | 2327 | 1.3109 | {'f1': 0.8333333333333334} | {'accuracy': 0.8272} |
| 0.0006 | 180.0 | 2340 | 1.3097 | {'f1': 0.8330745341614907} | {'accuracy': 0.828} |
| 0.0006 | 181.0 | 2353 | 1.3097 | {'f1': 0.8332685581033813} | {'accuracy': 0.8284} |
| 0.0006 | 182.0 | 2366 | 1.3102 | {'f1': 0.8332685581033813} | {'accuracy': 0.8284} |
| 0.0006 | 183.0 | 2379 | 1.3107 | {'f1': 0.8332685581033813} | {'accuracy': 0.8284} |
| 0.0006 | 184.0 | 2392 | 1.3111 | {'f1': 0.8332685581033813} | {'accuracy': 0.8284} |
| 0.0006 | 185.0 | 2405 | 1.3115 | {'f1': 0.8332685581033813} | {'accuracy': 0.8284} |
| 0.0006 | 186.0 | 2418 | 1.3121 | {'f1': 0.8337218337218338} | {'accuracy': 0.8288} |
| 0.0006 | 187.0 | 2431 | 1.3124 | {'f1': 0.8332685581033813} | {'accuracy': 0.8284} |
| 0.0006 | 188.0 | 2444 | 1.3125 | {'f1': 0.8331388564760793} | {'accuracy': 0.8284} |
| 0.0006 | 189.0 | 2457 | 1.3129 | {'f1': 0.8325545171339565} | {'accuracy': 0.828} |
| 0.0006 | 190.0 | 2470 | 1.3134 | {'f1': 0.8330089528999609} | {'accuracy': 0.8284} |
| 0.0006 | 191.0 | 2483 | 1.3139 | {'f1': 0.8331388564760793} | {'accuracy': 0.8284} |
| 0.0006 | 192.0 | 2496 | 1.3150 | {'f1': 0.8333980582524272} | {'accuracy': 0.8284} |
| 0.0026 | 193.0 | 2509 | 1.3162 | {'f1': 0.8332688588007737} | {'accuracy': 0.8276} |
| 0.0026 | 194.0 | 2522 | 1.3166 | {'f1': 0.8335913312693498} | {'accuracy': 0.828} |
| 0.0026 | 195.0 | 2535 | 1.3170 | {'f1': 0.8337853545137544} | {'accuracy': 0.8284} |
| 0.0026 | 196.0 | 2548 | 1.3174 | {'f1': 0.8337853545137544} | {'accuracy': 0.8284} |
| 0.0026 | 197.0 | 2561 | 1.3177 | {'f1': 0.8343034536282499} | {'accuracy': 0.8292} |
| 0.0026 | 198.0 | 2574 | 1.3185 | {'f1': 0.8329466357308585} | {'accuracy': 0.8272} |
| 0.0026 | 199.0 | 2587 | 1.3179 | {'f1': 0.83125} | {'accuracy': 0.8272} |
| 0.0026 | 200.0 | 2600 | 1.3191 | {'f1': 0.8277755982738328} | {'accuracy': 0.8244} |
| 0.0026 | 201.0 | 2613 | 1.3336 | {'f1': 0.8183648811921063} | {'accuracy': 0.8196} |
| 0.0026 | 202.0 | 2626 | 1.3417 | {'f1': 0.8191403081914032} | {'accuracy': 0.8216} |
| 0.0026 | 203.0 | 2639 | 1.3388 | {'f1': 0.8192478770723818} | {'accuracy': 0.8212} |
| 0.0026 | 204.0 | 2652 | 1.3314 | {'f1': 0.8196062675773402} | {'accuracy': 0.8204} |
| 0.0026 | 205.0 | 2665 | 1.3284 | {'f1': 0.820943245403677} | {'accuracy': 0.8208} |
| 0.0026 | 206.0 | 2678 | 1.3247 | {'f1': 0.8220372572334523} | {'accuracy': 0.8204} |
| 0.0026 | 207.0 | 2691 | 1.3207 | {'f1': 0.8256087981146898} | {'accuracy': 0.8224} |
| 0.0026 | 208.0 | 2704 | 1.3203 | {'f1': 0.8326848249027238} | {'accuracy': 0.828} |
| 0.0026 | 209.0 | 2717 | 1.3791 | {'f1': 0.8407079646017698} | {'accuracy': 0.8272} |
| 0.0026 | 210.0 | 2730 | 1.3041 | {'f1': 0.8251521298174442} | {'accuracy': 0.8276} |
| 0.0026 | 211.0 | 2743 | 1.2786 | {'f1': 0.8385376999238386} | {'accuracy': 0.8304} |
| 0.0026 | 212.0 | 2756 | 1.2782 | {'f1': 0.8351477449455676} | {'accuracy': 0.8304} |
| 0.0026 | 213.0 | 2769 | 1.3099 | {'f1': 0.8365566932119834} | {'accuracy': 0.8276} |
| 0.0026 | 214.0 | 2782 | 1.3136 | {'f1': 0.8366805608184918} | {'accuracy': 0.8276} |
| 0.0026 | 215.0 | 2795 | 1.3667 | {'f1': 0.8195488721804511} | {'accuracy': 0.8272} |
| 0.0026 | 216.0 | 2808 | 1.3974 | {'f1': 0.8440899202320521} | {'accuracy': 0.828} |
| 0.0026 | 217.0 | 2821 | 1.4398 | {'f1': 0.8103225806451613} | {'accuracy': 0.8236} |
| 0.0026 | 218.0 | 2834 | 1.7629 | {'f1': 0.8198630136986301} | {'accuracy': 0.7896} |
| 0.0026 | 219.0 | 2847 | 1.5840 | {'f1': 0.7884532529082292} | {'accuracy': 0.8036} |
| 0.0026 | 220.0 | 2860 | 1.4395 | {'f1': 0.8162460567823343} | {'accuracy': 0.8136} |
| 0.0026 | 221.0 | 2873 | 1.4460 | {'f1': 0.8201160541586073} | {'accuracy': 0.814} |
| 0.0026 | 222.0 | 2886 | 1.6072 | {'f1': 0.7866036925719193} | {'accuracy': 0.8012} |
| 0.0026 | 223.0 | 2899 | 1.4704 | {'f1': 0.816967792615868} | {'accuracy': 0.8136} |
| 0.0026 | 224.0 | 2912 | 1.4871 | {'f1': 0.8194233687405159} | {'accuracy': 0.8096} |
| 0.0026 | 225.0 | 2925 | 1.4758 | {'f1': 0.8201771274547555} | {'accuracy': 0.8132} |
| 0.0026 | 226.0 | 2938 | 1.4734 | {'f1': 0.8220338983050847} | {'accuracy': 0.8152} |
| 0.0026 | 227.0 | 2951 | 1.4819 | {'f1': 0.8214285714285715} | {'accuracy': 0.812} |
| 0.0026 | 228.0 | 2964 | 1.4753 | {'f1': 0.8232149675448645} | {'accuracy': 0.8148} |
| 0.0026 | 229.0 | 2977 | 1.4708 | {'f1': 0.8227168073676132} | {'accuracy': 0.8152} |
| 0.0026 | 230.0 | 2990 | 1.4720 | {'f1': 0.8226733052470317} | {'accuracy': 0.8148} |
| 0.0018 | 231.0 | 3003 | 1.4702 | {'f1': 0.8263056092843328} | {'accuracy': 0.8204} |
| 0.0018 | 232.0 | 3016 | 1.4794 | {'f1': 0.8153420324238829} | {'accuracy': 0.8132} |
| 0.0018 | 233.0 | 3029 | 1.4842 | {'f1': 0.8144616607071911} | {'accuracy': 0.8132} |
| 0.0018 | 234.0 | 3042 | 1.6032 | {'f1': 0.7861420017108641} | {'accuracy': 0.8} |
| 0.0018 | 235.0 | 3055 | 1.4249 | {'f1': 0.8261376896149358} | {'accuracy': 0.8212} |
| 0.0018 | 236.0 | 3068 | 1.5246 | {'f1': 0.8329718004338396} | {'accuracy': 0.8152} |
| 0.0018 | 237.0 | 3081 | 1.4204 | {'f1': 0.8286792452830187} | {'accuracy': 0.8184} |
| 0.0018 | 238.0 | 3094 | 1.4120 | {'f1': 0.8244575936883628} | {'accuracy': 0.822} |
| 0.0018 | 239.0 | 3107 | 1.4170 | {'f1': 0.8208184346444181} | {'accuracy': 0.8196} |
| 0.0018 | 240.0 | 3120 | 1.4178 | {'f1': 0.8211446740858506} | {'accuracy': 0.82} |
| 0.0018 | 241.0 | 3133 | 1.4179 | {'f1': 0.8208184346444181} | {'accuracy': 0.8196} |
| 0.0018 | 242.0 | 3146 | 1.4176 | {'f1': 0.8217546645494245} | {'accuracy': 0.8204} |
| 0.0018 | 243.0 | 3159 | 1.4175 | {'f1': 0.8222222222222223} | {'accuracy': 0.8208} |
| 0.0018 | 244.0 | 3172 | 1.4175 | {'f1': 0.8226894089646964} | {'accuracy': 0.8212} |
| 0.0018 | 245.0 | 3185 | 1.4173 | {'f1': 0.8234362628661915} | {'accuracy': 0.8216} |
| 0.0018 | 246.0 | 3198 | 1.4170 | {'f1': 0.8231104075979422} | {'accuracy': 0.8212} |
| 0.0018 | 247.0 | 3211 | 1.4172 | {'f1': 0.8231104075979422} | {'accuracy': 0.8212} |
| 0.0018 | 248.0 | 3224 | 1.4170 | {'f1': 0.8235759493670886} | {'accuracy': 0.8216} |
| 0.0018 | 249.0 | 3237 | 1.4166 | {'f1': 0.8237154150197629} | {'accuracy': 0.8216} |
| 0.0018 | 250.0 | 3250 | 1.4157 | {'f1': 0.824782951854775} | {'accuracy': 0.8224} |
| 0.0018 | 251.0 | 3263 | 1.4147 | {'f1': 0.8258471237194642} | {'accuracy': 0.8232} |
| 0.0018 | 252.0 | 3276 | 1.4139 | {'f1': 0.8267716535433071} | {'accuracy': 0.824} |
| 0.0018 | 253.0 | 3289 | 1.4158 | {'f1': 0.8263095706971249} | {'accuracy': 0.8236} |
| 0.0018 | 254.0 | 3302 | 1.4164 | {'f1': 0.8263095706971249} | {'accuracy': 0.8236} |
| 0.0018 | 255.0 | 3315 | 1.4164 | {'f1': 0.8263095706971249} | {'accuracy': 0.8236} |
| 0.0018 | 256.0 | 3328 | 1.4163 | {'f1': 0.8263095706971249} | {'accuracy': 0.8236} |
| 0.0018 | 257.0 | 3341 | 1.4156 | {'f1': 0.8275049115913556} | {'accuracy': 0.8244} |
| 0.0018 | 258.0 | 3354 | 1.4152 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0018 | 259.0 | 3367 | 1.4152 | {'f1': 0.8271266170129361} | {'accuracy': 0.8236} |
| 0.0018 | 260.0 | 3380 | 1.4151 | {'f1': 0.8261550509005481} | {'accuracy': 0.8224} |
| 0.0018 | 261.0 | 3393 | 1.4152 | {'f1': 0.8258317025440313} | {'accuracy': 0.822} |
| 0.0018 | 262.0 | 3406 | 1.4153 | {'f1': 0.8262910798122066} | {'accuracy': 0.8224} |
| 0.0018 | 263.0 | 3419 | 1.4156 | {'f1': 0.8261550509005481} | {'accuracy': 0.8224} |
| 0.0018 | 264.0 | 3432 | 1.4160 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0018 | 265.0 | 3445 | 1.4161 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0018 | 266.0 | 3458 | 1.4163 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0018 | 267.0 | 3471 | 1.4170 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0018 | 268.0 | 3484 | 1.4173 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0018 | 269.0 | 3497 | 1.4182 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0009 | 270.0 | 3510 | 1.4186 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0009 | 271.0 | 3523 | 1.4189 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0009 | 272.0 | 3536 | 1.4194 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0009 | 273.0 | 3549 | 1.4196 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0009 | 274.0 | 3562 | 1.4197 | {'f1': 0.8276403612092659} | {'accuracy': 0.8244} |
| 0.0009 | 275.0 | 3575 | 1.4195 | {'f1': 0.8277755982738328} | {'accuracy': 0.8244} |
| 0.0009 | 276.0 | 3588 | 1.4196 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0009 | 277.0 | 3601 | 1.4200 | {'f1': 0.8271266170129361} | {'accuracy': 0.8236} |
| 0.0009 | 278.0 | 3614 | 1.4201 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0009 | 279.0 | 3627 | 1.4203 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0009 | 280.0 | 3640 | 1.4205 | {'f1': 0.8268025078369906} | {'accuracy': 0.8232} |
| 0.0009 | 281.0 | 3653 | 1.4205 | {'f1': 0.827073552425665} | {'accuracy': 0.8232} |
| 0.0009 | 282.0 | 3666 | 1.4207 | {'f1': 0.8272087568412823} | {'accuracy': 0.8232} |
| 0.0009 | 283.0 | 3679 | 1.4209 | {'f1': 0.8273972602739728} | {'accuracy': 0.8236} |
| 0.0009 | 284.0 | 3692 | 1.4212 | {'f1': 0.8273972602739728} | {'accuracy': 0.8236} |
| 0.0009 | 285.0 | 3705 | 1.4213 | {'f1': 0.827073552425665} | {'accuracy': 0.8232} |
| 0.0009 | 286.0 | 3718 | 1.4215 | {'f1': 0.8275322643723112} | {'accuracy': 0.8236} |
| 0.0009 | 287.0 | 3731 | 1.4216 | {'f1': 0.8272087568412823} | {'accuracy': 0.8232} |
| 0.0009 | 288.0 | 3744 | 1.4214 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0009 | 289.0 | 3757 | 1.4214 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0009 | 290.0 | 3770 | 1.4208 | {'f1': 0.828361100348702} | {'accuracy': 0.8228} |
| 0.0009 | 291.0 | 3783 | 1.4212 | {'f1': 0.8287591805179745} | {'accuracy': 0.8228} |
| 0.0009 | 292.0 | 3796 | 1.4214 | {'f1': 0.8287591805179745} | {'accuracy': 0.8228} |
| 0.0009 | 293.0 | 3809 | 1.4217 | {'f1': 0.8292117465224111} | {'accuracy': 0.8232} |
| 0.0009 | 294.0 | 3822 | 1.4218 | {'f1': 0.8290796597061098} | {'accuracy': 0.8232} |
| 0.0009 | 295.0 | 3835 | 1.4220 | {'f1': 0.8287591805179745} | {'accuracy': 0.8228} |
| 0.0009 | 296.0 | 3848 | 1.4223 | {'f1': 0.8292117465224111} | {'accuracy': 0.8232} |
| 0.0009 | 297.0 | 3861 | 1.4227 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0009 | 298.0 | 3874 | 1.4229 | {'f1': 0.8290235430335777} | {'accuracy': 0.8228} |
| 0.0009 | 299.0 | 3887 | 1.4230 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0009 | 300.0 | 3900 | 1.4232 | {'f1': 0.8292117465224111} | {'accuracy': 0.8232} |
| 0.0009 | 301.0 | 3913 | 1.4233 | {'f1': 0.8287591805179745} | {'accuracy': 0.8228} |
| 0.0009 | 302.0 | 3926 | 1.4286 | {'f1': 0.8276947285601889} | {'accuracy': 0.8248} |
| 0.0009 | 303.0 | 3939 | 1.4324 | {'f1': 0.8245059288537548} | {'accuracy': 0.8224} |
| 0.0009 | 304.0 | 3952 | 1.4288 | {'f1': 0.8276947285601889} | {'accuracy': 0.8248} |
| 0.0009 | 305.0 | 3965 | 1.4255 | {'f1': 0.8277755982738328} | {'accuracy': 0.8244} |
| 0.0009 | 306.0 | 3978 | 1.4249 | {'f1': 0.8275862068965518} | {'accuracy': 0.824} |
| 0.0009 | 307.0 | 3991 | 1.4248 | {'f1': 0.8275862068965518} | {'accuracy': 0.824} |
| 0.0 | 308.0 | 4004 | 1.4249 | {'f1': 0.8280454367410889} | {'accuracy': 0.8244} |
| 0.0 | 309.0 | 4017 | 1.4257 | {'f1': 0.8275862068965518} | {'accuracy': 0.824} |
| 0.0 | 310.0 | 4030 | 1.4276 | {'f1': 0.8281004709576137} | {'accuracy': 0.8248} |
| 0.0 | 311.0 | 4043 | 1.4281 | {'f1': 0.8282907662082514} | {'accuracy': 0.8252} |
| 0.0 | 312.0 | 4056 | 1.4282 | {'f1': 0.8281004709576137} | {'accuracy': 0.8248} |
| 0.0 | 313.0 | 4069 | 1.4253 | {'f1': 0.8277474668745128} | {'accuracy': 0.8232} |
| 0.0 | 314.0 | 4082 | 1.4248 | {'f1': 0.827906976744186} | {'accuracy': 0.8224} |
| 0.0 | 315.0 | 4095 | 1.4250 | {'f1': 0.8288148721920992} | {'accuracy': 0.8232} |
| 0.0 | 316.0 | 4108 | 1.4252 | {'f1': 0.8292682926829268} | {'accuracy': 0.8236} |
| 0.0 | 317.0 | 4121 | 1.4253 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0 | 318.0 | 4134 | 1.4255 | {'f1': 0.8301158301158301} | {'accuracy': 0.824} |
| 0.0 | 319.0 | 4147 | 1.4257 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0 | 320.0 | 4160 | 1.4259 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0 | 321.0 | 4173 | 1.4261 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0 | 322.0 | 4186 | 1.4265 | {'f1': 0.8308285163776494} | {'accuracy': 0.8244} |
| 0.0 | 323.0 | 4199 | 1.4269 | {'f1': 0.8309587986137851} | {'accuracy': 0.8244} |
| 0.0 | 324.0 | 4212 | 1.4272 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 325.0 | 4225 | 1.4275 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 326.0 | 4238 | 1.4277 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 327.0 | 4251 | 1.4278 | {'f1': 0.8309587986137851} | {'accuracy': 0.8244} |
| 0.0 | 328.0 | 4264 | 1.4279 | {'f1': 0.8305084745762712} | {'accuracy': 0.824} |
| 0.0 | 329.0 | 4277 | 1.4281 | {'f1': 0.8305084745762712} | {'accuracy': 0.824} |
| 0.0 | 330.0 | 4290 | 1.4283 | {'f1': 0.8305084745762712} | {'accuracy': 0.824} |
| 0.0 | 331.0 | 4303 | 1.4286 | {'f1': 0.8314693405322021} | {'accuracy': 0.8252} |
| 0.0 | 332.0 | 4316 | 1.4288 | {'f1': 0.8296639629200464} | {'accuracy': 0.8236} |
| 0.0 | 333.0 | 4329 | 1.4290 | {'f1': 0.8298530549110597} | {'accuracy': 0.824} |
| 0.0 | 334.0 | 4342 | 1.4292 | {'f1': 0.8301740812379111} | {'accuracy': 0.8244} |
| 0.0 | 335.0 | 4355 | 1.4295 | {'f1': 0.8297213622291022} | {'accuracy': 0.824} |
| 0.0 | 336.0 | 4368 | 1.4295 | {'f1': 0.8288148721920992} | {'accuracy': 0.8232} |
| 0.0 | 337.0 | 4381 | 1.4292 | {'f1': 0.8285492629945695} | {'accuracy': 0.8232} |
| 0.0 | 338.0 | 4394 | 1.4293 | {'f1': 0.8285492629945695} | {'accuracy': 0.8232} |
| 0.0 | 339.0 | 4407 | 1.4293 | {'f1': 0.828361100348702} | {'accuracy': 0.8228} |
| 0.0 | 340.0 | 4420 | 1.4294 | {'f1': 0.8301740812379111} | {'accuracy': 0.8244} |
| 0.0 | 341.0 | 4433 | 1.4298 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 342.0 | 4446 | 1.4301 | {'f1': 0.8310888803385917} | {'accuracy': 0.8244} |
| 0.0 | 343.0 | 4459 | 1.4303 | {'f1': 0.8315384615384616} | {'accuracy': 0.8248} |
| 0.0 | 344.0 | 4472 | 1.4307 | {'f1': 0.8315384615384616} | {'accuracy': 0.8248} |
| 0.0 | 345.0 | 4485 | 1.4306 | {'f1': 0.8315384615384616} | {'accuracy': 0.8248} |
| 0.0 | 346.0 | 4498 | 1.4303 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 347.0 | 4511 | 1.4293 | {'f1': 0.8304364619544227} | {'accuracy': 0.8244} |
| 0.0 | 348.0 | 4524 | 1.4292 | {'f1': 0.8301740812379111} | {'accuracy': 0.8244} |
| 0.0 | 349.0 | 4537 | 1.4293 | {'f1': 0.8301740812379111} | {'accuracy': 0.8244} |
| 0.0 | 350.0 | 4550 | 1.4294 | {'f1': 0.8313392512543419} | {'accuracy': 0.8252} |
| 0.0 | 351.0 | 4563 | 1.4277 | {'f1': 0.8278274387874076} | {'accuracy': 0.8228} |
| 0.0 | 352.0 | 4576 | 1.4276 | {'f1': 0.8275593616193071} | {'accuracy': 0.8228} |
| 0.0 | 353.0 | 4589 | 1.4276 | {'f1': 0.827425009738995} | {'accuracy': 0.8228} |
| 0.0 | 354.0 | 4602 | 1.4278 | {'f1': 0.8277474668745128} | {'accuracy': 0.8232} |
| 0.0 | 355.0 | 4615 | 1.4275 | {'f1': 0.8304364619544227} | {'accuracy': 0.8244} |
| 0.0 | 356.0 | 4628 | 1.4281 | {'f1': 0.8306980331662168} | {'accuracy': 0.8244} |
| 0.0 | 357.0 | 4641 | 1.4286 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 358.0 | 4654 | 1.4287 | {'f1': 0.8312788906009245} | {'accuracy': 0.8248} |
| 0.0 | 359.0 | 4667 | 1.4289 | {'f1': 0.8314087759815243} | {'accuracy': 0.8248} |
| 0.0 | 360.0 | 4680 | 1.4291 | {'f1': 0.8312788906009245} | {'accuracy': 0.8248} |
| 0.0 | 361.0 | 4693 | 1.4291 | {'f1': 0.8311488049344642} | {'accuracy': 0.8248} |
| 0.0 | 362.0 | 4706 | 1.4292 | {'f1': 0.8311488049344642} | {'accuracy': 0.8248} |
| 0.0 | 363.0 | 4719 | 1.4383 | {'f1': 0.8281557215886748} | {'accuracy': 0.8252} |
| 0.0 | 364.0 | 4732 | 1.4445 | {'f1': 0.8256735340729001} | {'accuracy': 0.824} |
| 0.0 | 365.0 | 4745 | 1.4462 | {'f1': 0.8252080856123662} | {'accuracy': 0.8236} |
| 0.0 | 366.0 | 4758 | 1.4463 | {'f1': 0.8252080856123662} | {'accuracy': 0.8236} |
| 0.0 | 367.0 | 4771 | 1.4441 | {'f1': 0.8260869565217391} | {'accuracy': 0.824} |
| 0.0 | 368.0 | 4784 | 1.4435 | {'f1': 0.8271507498026835} | {'accuracy': 0.8248} |
| 0.0 | 369.0 | 4797 | 1.4432 | {'f1': 0.8276134122287968} | {'accuracy': 0.8252} |
| 0.0 | 370.0 | 4810 | 1.4429 | {'f1': 0.8272870662460567} | {'accuracy': 0.8248} |
| 0.0 | 371.0 | 4823 | 1.4427 | {'f1': 0.8278849940921623} | {'accuracy': 0.8252} |
| 0.0 | 372.0 | 4836 | 1.4426 | {'f1': 0.8280204643841007} | {'accuracy': 0.8252} |
| 0.0 | 373.0 | 4849 | 1.4426 | {'f1': 0.8280204643841007} | {'accuracy': 0.8252} |
| 0.0 | 374.0 | 4862 | 1.4428 | {'f1': 0.8280204643841007} | {'accuracy': 0.8252} |
| 0.0 | 375.0 | 4875 | 1.4415 | {'f1': 0.8278301886792453} | {'accuracy': 0.8248} |
| 0.0 | 376.0 | 4888 | 1.4412 | {'f1': 0.828750981932443} | {'accuracy': 0.8256} |
| 0.0 | 377.0 | 4901 | 1.4416 | {'f1': 0.8278301886792453} | {'accuracy': 0.8248} |
| 0.0 | 378.0 | 4914 | 1.4506 | {'f1': 0.8247914183551848} | {'accuracy': 0.8236} |
| 0.0 | 379.0 | 4927 | 1.4528 | {'f1': 0.8253083963390371} | {'accuracy': 0.8244} |
| 0.0 | 380.0 | 4940 | 1.4528 | {'f1': 0.8253083963390371} | {'accuracy': 0.8244} |
| 0.0 | 381.0 | 4953 | 1.4527 | {'f1': 0.8253083963390371} | {'accuracy': 0.8244} |
| 0.0 | 382.0 | 4966 | 1.4519 | {'f1': 0.8251192368839427} | {'accuracy': 0.824} |
| 0.0 | 383.0 | 4979 | 1.4503 | {'f1': 0.8261386138613862} | {'accuracy': 0.8244} |
| 0.0 | 384.0 | 4992 | 1.4494 | {'f1': 0.8264136022143139} | {'accuracy': 0.8244} |
| 0.0 | 385.0 | 5005 | 1.4490 | {'f1': 0.8274772996446901} | {'accuracy': 0.8252} |
| 0.0 | 386.0 | 5018 | 1.4483 | {'f1': 0.8279400157853197} | {'accuracy': 0.8256} |
| 0.0 | 387.0 | 5031 | 1.4479 | {'f1': 0.8272870662460567} | {'accuracy': 0.8248} |
| 0.0 | 388.0 | 5044 | 1.4477 | {'f1': 0.8274231678486997} | {'accuracy': 0.8248} |
| 0.0 | 389.0 | 5057 | 1.4471 | {'f1': 0.8281557215886748} | {'accuracy': 0.8252} |
| 0.0 | 390.0 | 5070 | 1.4501 | {'f1': 0.8274772996446901} | {'accuracy': 0.8252} |
| 0.0 | 391.0 | 5083 | 1.4676 | {'f1': 0.8250401284109149} | {'accuracy': 0.8256} |
| 0.0 | 392.0 | 5096 | 1.4718 | {'f1': 0.8244766505636072} | {'accuracy': 0.8256} |
| 0.0 | 393.0 | 5109 | 1.4718 | {'f1': 0.8249496981891349} | {'accuracy': 0.826} |
| 0.0 | 394.0 | 5122 | 1.4704 | {'f1': 0.8252310164724789} | {'accuracy': 0.826} |
| 0.0 | 395.0 | 5135 | 1.4684 | {'f1': 0.8247091857200161} | {'accuracy': 0.8252} |
| 0.0 | 396.0 | 5148 | 1.4633 | {'f1': 0.8258785942492013} | {'accuracy': 0.8256} |
| 0.0 | 397.0 | 5161 | 1.4528 | {'f1': 0.8270676691729324} | {'accuracy': 0.8252} |
| 0.0 | 398.0 | 5174 | 1.4502 | {'f1': 0.8285376428852975} | {'accuracy': 0.826} |
| 0.0 | 399.0 | 5187 | 1.4493 | {'f1': 0.8276947285601889} | {'accuracy': 0.8248} |
| 0.0 | 400.0 | 5200 | 1.4476 | {'f1': 0.8263943440691282} | {'accuracy': 0.8232} |
| 0.0 | 401.0 | 5213 | 1.4489 | {'f1': 0.8289992119779355} | {'accuracy': 0.8264} |
| 0.0 | 402.0 | 5226 | 1.4496 | {'f1': 0.8290564547966838} | {'accuracy': 0.8268} |
| 0.0 | 403.0 | 5239 | 1.4494 | {'f1': 0.829518547750592} | {'accuracy': 0.8272} |
| 0.0 | 404.0 | 5252 | 1.4496 | {'f1': 0.8291913214990139} | {'accuracy': 0.8268} |
| 0.0 | 405.0 | 5265 | 1.4496 | {'f1': 0.8291913214990139} | {'accuracy': 0.8268} |
| 0.0 | 406.0 | 5278 | 1.4494 | {'f1': 0.8289992119779355} | {'accuracy': 0.8264} |
| 0.0 | 407.0 | 5291 | 1.4493 | {'f1': 0.828672705789681} | {'accuracy': 0.826} |
| 0.0 | 408.0 | 5304 | 1.4491 | {'f1': 0.8283464566929134} | {'accuracy': 0.8256} |
| 0.0 | 409.0 | 5317 | 1.4490 | {'f1': 0.8280204643841007} | {'accuracy': 0.8252} |
| 0.0 | 410.0 | 5330 | 1.4491 | {'f1': 0.8280204643841007} | {'accuracy': 0.8252} |
| 0.0 | 411.0 | 5343 | 1.4495 | {'f1': 0.8280204643841007} | {'accuracy': 0.8252} |
| 0.0 | 412.0 | 5356 | 1.4494 | {'f1': 0.8276947285601889} | {'accuracy': 0.8248} |
| 0.0 | 413.0 | 5369 | 1.4493 | {'f1': 0.8276947285601889} | {'accuracy': 0.8248} |
| 0.0 | 414.0 | 5382 | 1.4493 | {'f1': 0.8273692489186001} | {'accuracy': 0.8244} |
| 0.0 | 415.0 | 5395 | 1.4493 | {'f1': 0.8278301886792453} | {'accuracy': 0.8248} |
| 0.0 | 416.0 | 5408 | 1.4493 | {'f1': 0.8279654359780046} | {'accuracy': 0.8248} |
| 0.0 | 417.0 | 5421 | 1.4492 | {'f1': 0.8279654359780046} | {'accuracy': 0.8248} |
| 0.0 | 418.0 | 5434 | 1.4493 | {'f1': 0.8273155416012559} | {'accuracy': 0.824} |
| 0.0 | 419.0 | 5447 | 1.4493 | {'f1': 0.8273155416012559} | {'accuracy': 0.824} |
| 0.0 | 420.0 | 5460 | 1.4493 | {'f1': 0.828235294117647} | {'accuracy': 0.8248} |
| 0.0 | 421.0 | 5473 | 1.4493 | {'f1': 0.828235294117647} | {'accuracy': 0.8248} |
| 0.0 | 422.0 | 5486 | 1.4498 | {'f1': 0.8273155416012559} | {'accuracy': 0.824} |
| 0.0 | 423.0 | 5499 | 1.4499 | {'f1': 0.8273155416012559} | {'accuracy': 0.824} |
| 0.0 | 424.0 | 5512 | 1.4499 | {'f1': 0.828235294117647} | {'accuracy': 0.8248} |
| 0.0 | 425.0 | 5525 | 1.4504 | {'f1': 0.8273155416012559} | {'accuracy': 0.824} |
| 0.0 | 426.0 | 5538 | 1.4507 | {'f1': 0.8273155416012559} | {'accuracy': 0.824} |
| 0.0 | 427.0 | 5551 | 1.4472 | {'f1': 0.8306264501160093} | {'accuracy': 0.8248} |
| 0.0 | 428.0 | 5564 | 1.4526 | {'f1': 0.8352402745995423} | {'accuracy': 0.8272} |
| 0.0 | 429.0 | 5577 | 1.4554 | {'f1': 0.8340296240030383} | {'accuracy': 0.8252} |
| 0.0 | 430.0 | 5590 | 1.4555 | {'f1': 0.8340296240030383} | {'accuracy': 0.8252} |
| 0.0 | 431.0 | 5603 | 1.4543 | {'f1': 0.8337771015595283} | {'accuracy': 0.8252} |
| 0.0 | 432.0 | 5616 | 1.4541 | {'f1': 0.8340943683409437} | {'accuracy': 0.8256} |
| 0.0 | 433.0 | 5629 | 1.4541 | {'f1': 0.8344118766653978} | {'accuracy': 0.826} |
| 0.0 | 434.0 | 5642 | 1.4510 | {'f1': 0.832183908045977} | {'accuracy': 0.8248} |
| 0.0 | 435.0 | 5655 | 1.4500 | {'f1': 0.83} | {'accuracy': 0.8232} |
| 0.0 | 436.0 | 5668 | 1.4497 | {'f1': 0.829738058551618} | {'accuracy': 0.8232} |
| 0.0 | 437.0 | 5681 | 1.4486 | {'f1': 0.8279106232849863} | {'accuracy': 0.8244} |
| 0.0 | 438.0 | 5694 | 1.4499 | {'f1': 0.8274231678486997} | {'accuracy': 0.8248} |
| 0.0 | 439.0 | 5707 | 1.4504 | {'f1': 0.8284023668639053} | {'accuracy': 0.826} |
| 0.0 | 440.0 | 5720 | 1.4504 | {'f1': 0.8284023668639053} | {'accuracy': 0.826} |
| 0.0 | 441.0 | 5733 | 1.4504 | {'f1': 0.8280757097791798} | {'accuracy': 0.8256} |
| 0.0 | 442.0 | 5746 | 1.4504 | {'f1': 0.8277493102089082} | {'accuracy': 0.8252} |
| 0.0 | 443.0 | 5759 | 1.4502 | {'f1': 0.8275590551181103} | {'accuracy': 0.8248} |
| 0.0 | 444.0 | 5772 | 1.4500 | {'f1': 0.828616352201258} | {'accuracy': 0.8256} |
| 0.0 | 445.0 | 5785 | 1.4498 | {'f1': 0.8282907662082514} | {'accuracy': 0.8252} |
| 0.0 | 446.0 | 5798 | 1.4498 | {'f1': 0.8282907662082514} | {'accuracy': 0.8252} |
| 0.0 | 447.0 | 5811 | 1.4499 | {'f1': 0.8282907662082514} | {'accuracy': 0.8252} |
| 0.0 | 448.0 | 5824 | 1.4499 | {'f1': 0.8279654359780046} | {'accuracy': 0.8248} |
| 0.0 | 449.0 | 5837 | 1.4500 | {'f1': 0.8279654359780046} | {'accuracy': 0.8248} |
| 0.0 | 450.0 | 5850 | 1.4500 | {'f1': 0.8284255987436199} | {'accuracy': 0.8252} |
| 0.0 | 451.0 | 5863 | 1.4500 | {'f1': 0.8281004709576137} | {'accuracy': 0.8248} |
| 0.0 | 452.0 | 5876 | 1.4500 | {'f1': 0.8283699059561128} | {'accuracy': 0.8248} |
| 0.0 | 453.0 | 5889 | 1.4500 | {'f1': 0.8277212216131559} | {'accuracy': 0.824} |
| 0.0 | 454.0 | 5902 | 1.4501 | {'f1': 0.8281800391389432} | {'accuracy': 0.8244} |
| 0.0 | 455.0 | 5915 | 1.4501 | {'f1': 0.8281800391389432} | {'accuracy': 0.8244} |
| 0.0 | 456.0 | 5928 | 1.4500 | {'f1': 0.8286384976525822} | {'accuracy': 0.8248} |
| 0.0 | 457.0 | 5941 | 1.4499 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 458.0 | 5954 | 1.4499 | {'f1': 0.8266978922716627} | {'accuracy': 0.8224} |
| 0.0 | 459.0 | 5967 | 1.4500 | {'f1': 0.8271556769410847} | {'accuracy': 0.8228} |
| 0.0 | 460.0 | 5980 | 1.4501 | {'f1': 0.826833073322933} | {'accuracy': 0.8224} |
| 0.0 | 461.0 | 5993 | 1.4502 | {'f1': 0.8271556769410847} | {'accuracy': 0.8228} |
| 0.0 | 462.0 | 6006 | 1.4502 | {'f1': 0.826833073322933} | {'accuracy': 0.8224} |
| 0.0 | 463.0 | 6019 | 1.4503 | {'f1': 0.826833073322933} | {'accuracy': 0.8224} |
| 0.0 | 464.0 | 6032 | 1.4504 | {'f1': 0.826833073322933} | {'accuracy': 0.8224} |
| 0.0 | 465.0 | 6045 | 1.4506 | {'f1': 0.826833073322933} | {'accuracy': 0.8224} |
| 0.0 | 466.0 | 6058 | 1.4511 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 467.0 | 6071 | 1.4512 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 468.0 | 6084 | 1.4513 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 469.0 | 6097 | 1.4514 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 470.0 | 6110 | 1.4514 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 471.0 | 6123 | 1.4515 | {'f1': 0.8270206950409996} | {'accuracy': 0.8228} |
| 0.0 | 472.0 | 6136 | 1.4515 | {'f1': 0.8274785323965652} | {'accuracy': 0.8232} |
| 0.0 | 473.0 | 6149 | 1.4516 | {'f1': 0.8274785323965652} | {'accuracy': 0.8232} |
| 0.0 | 474.0 | 6162 | 1.4516 | {'f1': 0.8271556769410847} | {'accuracy': 0.8228} |
| 0.0 | 475.0 | 6175 | 1.4517 | {'f1': 0.8271556769410847} | {'accuracy': 0.8228} |
| 0.0 | 476.0 | 6188 | 1.4517 | {'f1': 0.8271556769410847} | {'accuracy': 0.8228} |
| 0.0 | 477.0 | 6201 | 1.4517 | {'f1': 0.8271556769410847} | {'accuracy': 0.8228} |
| 0.0 | 478.0 | 6214 | 1.4518 | {'f1': 0.82729044834308} | {'accuracy': 0.8228} |
| 0.0 | 479.0 | 6227 | 1.4518 | {'f1': 0.82729044834308} | {'accuracy': 0.8228} |
| 0.0 | 480.0 | 6240 | 1.4519 | {'f1': 0.82729044834308} | {'accuracy': 0.8228} |
| 0.0 | 481.0 | 6253 | 1.4519 | {'f1': 0.82729044834308} | {'accuracy': 0.8228} |
| 0.0 | 482.0 | 6266 | 1.4519 | {'f1': 0.82729044834308} | {'accuracy': 0.8228} |
| 0.0 | 483.0 | 6279 | 1.4520 | {'f1': 0.82729044834308} | {'accuracy': 0.8228} |
| 0.0 | 484.0 | 6292 | 1.4520 | {'f1': 0.8277474668745128} | {'accuracy': 0.8232} |
| 0.0 | 485.0 | 6305 | 1.4520 | {'f1': 0.8277474668745128} | {'accuracy': 0.8232} |
| 0.0 | 486.0 | 6318 | 1.4521 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 487.0 | 6331 | 1.4521 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 488.0 | 6344 | 1.4522 | {'f1': 0.8277474668745128} | {'accuracy': 0.8232} |
| 0.0 | 489.0 | 6357 | 1.4522 | {'f1': 0.8277474668745128} | {'accuracy': 0.8232} |
| 0.0 | 490.0 | 6370 | 1.4522 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 491.0 | 6383 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 492.0 | 6396 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 493.0 | 6409 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 494.0 | 6422 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 495.0 | 6435 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 496.0 | 6448 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 497.0 | 6461 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 498.0 | 6474 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 499.0 | 6487 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
| 0.0 | 500.0 | 6500 | 1.4523 | {'f1': 0.8282041293338528} | {'accuracy': 0.8236} |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
