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bert-base-uncased-test_16_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: 1.4509
- F1: {'f1': 0.795623290347792}
- Accuracy: {'accuracy': 0.7908}
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 | 0.6989 | {'f1': 0.5576858392196434} | {'accuracy': 0.474} |
| No log | 2.0 | 14 | 0.6959 | {'f1': 0.3558974358974359} | {'accuracy': 0.4976} |
| No log | 3.0 | 21 | 0.6937 | {'f1': 0.20887728459530028} | {'accuracy': 0.5152} |
| No log | 4.0 | 28 | 0.6882 | {'f1': 0.29038112522686027} | {'accuracy': 0.5308} |
| No log | 5.0 | 35 | 0.6813 | {'f1': 0.4042780748663102} | {'accuracy': 0.5544} |
| No log | 6.0 | 42 | 0.6713 | {'f1': 0.6004008016032063} | {'accuracy': 0.6012} |
| No log | 7.0 | 49 | 0.6636 | {'f1': 0.5108481262327416} | {'accuracy': 0.6032} |
| No log | 8.0 | 56 | 0.6479 | {'f1': 0.5741583257506825} | {'accuracy': 0.6256} |
| No log | 9.0 | 63 | 0.6377 | {'f1': 0.5568776772965255} | {'accuracy': 0.6276} |
| No log | 10.0 | 70 | 0.6173 | {'f1': 0.6251082251082252} | {'accuracy': 0.6536} |
| No log | 11.0 | 77 | 0.6139 | {'f1': 0.6412017167381974} | {'accuracy': 0.6656} |
| No log | 12.0 | 84 | 0.6046 | {'f1': 0.6773136773136773} | {'accuracy': 0.6848} |
| No log | 13.0 | 91 | 0.5947 | {'f1': 0.699759807846277} | {'accuracy': 0.7} |
| No log | 14.0 | 98 | 0.6145 | {'f1': 0.7012882447665056} | {'accuracy': 0.7032} |
| No log | 15.0 | 105 | 0.6231 | {'f1': 0.7110036275695284} | {'accuracy': 0.7132} |
| No log | 16.0 | 112 | 0.6193 | {'f1': 0.7321981424148607} | {'accuracy': 0.7232} |
| No log | 17.0 | 119 | 0.6490 | {'f1': 0.7197986577181208} | {'accuracy': 0.7328} |
| No log | 18.0 | 126 | 0.7302 | {'f1': 0.7480501865038997} | {'accuracy': 0.7028} |
| No log | 19.0 | 133 | 0.6882 | {'f1': 0.7303042934556065} | {'accuracy': 0.7412} |
| No log | 20.0 | 140 | 0.7858 | {'f1': 0.7411965811965813} | {'accuracy': 0.6972} |
| No log | 21.0 | 147 | 0.7169 | {'f1': 0.7423167848699764} | {'accuracy': 0.7384} |
| No log | 22.0 | 154 | 0.7337 | {'f1': 0.7538699690402476} | {'accuracy': 0.7456} |
| No log | 23.0 | 161 | 0.7640 | {'f1': 0.7517455391776571} | {'accuracy': 0.744} |
| No log | 24.0 | 168 | 0.7882 | {'f1': 0.7474747474747474} | {'accuracy': 0.75} |
| No log | 25.0 | 175 | 0.8841 | {'f1': 0.7670275934334614} | {'accuracy': 0.7332} |
| No log | 26.0 | 182 | 0.8158 | {'f1': 0.7625391849529781} | {'accuracy': 0.7576} |
| No log | 27.0 | 189 | 0.8408 | {'f1': 0.7698113207547169} | {'accuracy': 0.756} |
| No log | 28.0 | 196 | 0.8525 | {'f1': 0.7698776758409785} | {'accuracy': 0.7592} |
| No log | 29.0 | 203 | 0.9124 | {'f1': 0.7715959004392385} | {'accuracy': 0.7504} |
| No log | 30.0 | 210 | 0.8864 | {'f1': 0.7682737169517886} | {'accuracy': 0.7616} |
| No log | 31.0 | 217 | 0.9029 | {'f1': 0.7697063369397218} | {'accuracy': 0.7616} |
| No log | 32.0 | 224 | 1.0050 | {'f1': 0.7792022792022792} | {'accuracy': 0.752} |
| No log | 33.0 | 231 | 0.9465 | {'f1': 0.7596834652228238} | {'accuracy': 0.7692} |
| No log | 34.0 | 238 | 0.9308 | {'f1': 0.7711797307996834} | {'accuracy': 0.7688} |
| No log | 35.0 | 245 | 0.9586 | {'f1': 0.7802321228004493} | {'accuracy': 0.7652} |
| No log | 36.0 | 252 | 0.9790 | {'f1': 0.7811921510551647} | {'accuracy': 0.7636} |
| No log | 37.0 | 259 | 1.0231 | {'f1': 0.7810351067680059} | {'accuracy': 0.758} |
| No log | 38.0 | 266 | 0.9654 | {'f1': 0.7820464054773678} | {'accuracy': 0.7708} |
| No log | 39.0 | 273 | 0.9940 | {'f1': 0.7839940718784735} | {'accuracy': 0.7668} |
| No log | 40.0 | 280 | 1.0142 | {'f1': 0.781524926686217} | {'accuracy': 0.7616} |
| No log | 41.0 | 287 | 0.9734 | {'f1': 0.7821401077752117} | {'accuracy': 0.7736} |
| No log | 42.0 | 294 | 1.0272 | {'f1': 0.7828947368421054} | {'accuracy': 0.7624} |
| No log | 43.0 | 301 | 1.1120 | {'f1': 0.78005657708628} | {'accuracy': 0.7512} |
| No log | 44.0 | 308 | 0.9847 | {'f1': 0.7783094098883573} | {'accuracy': 0.7776} |
| No log | 45.0 | 315 | 1.1667 | {'f1': 0.7783558792924037} | {'accuracy': 0.7444} |
| No log | 46.0 | 322 | 0.9812 | {'f1': 0.7874509803921568} | {'accuracy': 0.7832} |
| No log | 47.0 | 329 | 0.9943 | {'f1': 0.7906441717791411} | {'accuracy': 0.7816} |
| No log | 48.0 | 336 | 1.0160 | {'f1': 0.7903650733910426} | {'accuracy': 0.7772} |
| No log | 49.0 | 343 | 0.9967 | {'f1': 0.7893903404592241} | {'accuracy': 0.7872} |
| No log | 50.0 | 350 | 1.0146 | {'f1': 0.7908571428571428} | {'accuracy': 0.7804} |
| No log | 51.0 | 357 | 1.2253 | {'f1': 0.7803187803187804} | {'accuracy': 0.7464} |
| No log | 52.0 | 364 | 1.1539 | {'f1': 0.7857651245551601} | {'accuracy': 0.7592} |
| No log | 53.0 | 371 | 1.0245 | {'f1': 0.7900466562986004} | {'accuracy': 0.784} |
| No log | 54.0 | 378 | 1.0217 | {'f1': 0.7878545745105873} | {'accuracy': 0.7876} |
| No log | 55.0 | 385 | 1.0649 | {'f1': 0.7927056196501675} | {'accuracy': 0.7772} |
| No log | 56.0 | 392 | 1.0459 | {'f1': 0.7894937190711839} | {'accuracy': 0.7788} |
| No log | 57.0 | 399 | 1.0372 | {'f1': 0.7898832684824902} | {'accuracy': 0.784} |
| No log | 58.0 | 406 | 1.0391 | {'f1': 0.78984375} | {'accuracy': 0.7848} |
| No log | 59.0 | 413 | 1.0456 | {'f1': 0.7893106119287374} | {'accuracy': 0.7824} |
| No log | 60.0 | 420 | 1.0520 | {'f1': 0.7903039630627166} | {'accuracy': 0.782} |
| No log | 61.0 | 427 | 1.0643 | {'f1': 0.7903963414634146} | {'accuracy': 0.78} |
| No log | 62.0 | 434 | 1.0581 | {'f1': 0.7901802838511699} | {'accuracy': 0.7812} |
| No log | 63.0 | 441 | 1.0626 | {'f1': 0.7914110429447851} | {'accuracy': 0.7824} |
| No log | 64.0 | 448 | 1.0688 | {'f1': 0.7949599083619702} | {'accuracy': 0.7852} |
| No log | 65.0 | 455 | 1.0655 | {'f1': 0.7936507936507937} | {'accuracy': 0.7868} |
| No log | 66.0 | 462 | 1.4237 | {'f1': 0.7764705882352941} | {'accuracy': 0.734} |
| No log | 67.0 | 469 | 1.1680 | {'f1': 0.7911530094271211} | {'accuracy': 0.7696} |
| No log | 68.0 | 476 | 1.0930 | {'f1': 0.7932618683001532} | {'accuracy': 0.784} |
| No log | 69.0 | 483 | 1.0860 | {'f1': 0.7917981072555204} | {'accuracy': 0.7888} |
| No log | 70.0 | 490 | 1.0901 | {'f1': 0.7907899960301707} | {'accuracy': 0.7892} |
| No log | 71.0 | 497 | 1.0984 | {'f1': 0.7927300850734724} | {'accuracy': 0.7856} |
| 0.1314 | 72.0 | 504 | 1.1386 | {'f1': 0.7922272047832587} | {'accuracy': 0.7776} |
| 0.1314 | 73.0 | 511 | 1.1201 | {'f1': 0.7810309278350516} | {'accuracy': 0.7876} |
| 0.1314 | 74.0 | 518 | 1.1620 | {'f1': 0.7892607576314822} | {'accuracy': 0.7708} |
| 0.1314 | 75.0 | 525 | 1.2650 | {'f1': 0.7887624466571835} | {'accuracy': 0.7624} |
| 0.1314 | 76.0 | 532 | 1.2139 | {'f1': 0.7917570498915402} | {'accuracy': 0.7696} |
| 0.1314 | 77.0 | 539 | 1.1546 | {'f1': 0.7930904994367254} | {'accuracy': 0.7796} |
| 0.1314 | 78.0 | 546 | 1.1401 | {'f1': 0.7948229920060906} | {'accuracy': 0.7844} |
| 0.1314 | 79.0 | 553 | 1.1279 | {'f1': 0.7967161845191557} | {'accuracy': 0.792} |
| 0.1314 | 80.0 | 560 | 1.1326 | {'f1': 0.7880741337630943} | {'accuracy': 0.7896} |
| 0.1314 | 81.0 | 567 | 1.1339 | {'f1': 0.790548658390068} | {'accuracy': 0.7908} |
| 0.1314 | 82.0 | 574 | 1.1349 | {'f1': 0.7938144329896908} | {'accuracy': 0.792} |
| 0.1314 | 83.0 | 581 | 1.1369 | {'f1': 0.793713163064833} | {'accuracy': 0.79} |
| 0.1314 | 84.0 | 588 | 1.1434 | {'f1': 0.7972136222910217} | {'accuracy': 0.7904} |
| 0.1314 | 85.0 | 595 | 1.1554 | {'f1': 0.7945101029355699} | {'accuracy': 0.7844} |
| 0.1314 | 86.0 | 602 | 1.1637 | {'f1': 0.7942402425161046} | {'accuracy': 0.7828} |
| 0.1314 | 87.0 | 609 | 1.1688 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.1314 | 88.0 | 616 | 1.1697 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.1314 | 89.0 | 623 | 1.1627 | {'f1': 0.7945205479452054} | {'accuracy': 0.784} |
| 0.1314 | 90.0 | 630 | 1.1591 | {'f1': 0.7943425076452599} | {'accuracy': 0.7848} |
| 0.1314 | 91.0 | 637 | 1.1593 | {'f1': 0.7946462715105163} | {'accuracy': 0.7852} |
| 0.1314 | 92.0 | 644 | 1.1609 | {'f1': 0.7946462715105163} | {'accuracy': 0.7852} |
| 0.1314 | 93.0 | 651 | 1.1630 | {'f1': 0.7940389759266335} | {'accuracy': 0.7844} |
| 0.1314 | 94.0 | 658 | 1.1652 | {'f1': 0.7937356760886172} | {'accuracy': 0.784} |
| 0.1314 | 95.0 | 665 | 1.1745 | {'f1': 0.7945309532852259} | {'accuracy': 0.7836} |
| 0.1314 | 96.0 | 672 | 1.1740 | {'f1': 0.7954372623574144} | {'accuracy': 0.7848} |
| 0.1314 | 97.0 | 679 | 1.1792 | {'f1': 0.795144157814871} | {'accuracy': 0.784} |
| 0.1314 | 98.0 | 686 | 1.1818 | {'f1': 0.7940953822861468} | {'accuracy': 0.7824} |
| 0.1314 | 99.0 | 693 | 1.1840 | {'f1': 0.7945516458569807} | {'accuracy': 0.7828} |
| 0.1314 | 100.0 | 700 | 1.1871 | {'f1': 0.7927519818799548} | {'accuracy': 0.7804} |
| 0.1314 | 101.0 | 707 | 1.1823 | {'f1': 0.7957479119210327} | {'accuracy': 0.7848} |
| 0.1314 | 102.0 | 714 | 1.1863 | {'f1': 0.7942402425161046} | {'accuracy': 0.7828} |
| 0.1314 | 103.0 | 721 | 1.1918 | {'f1': 0.7930513595166163} | {'accuracy': 0.7808} |
| 0.1314 | 104.0 | 728 | 1.1911 | {'f1': 0.7937949300037835} | {'accuracy': 0.782} |
| 0.1314 | 105.0 | 735 | 1.1899 | {'f1': 0.7942402425161046} | {'accuracy': 0.7828} |
| 0.1314 | 106.0 | 742 | 1.1899 | {'f1': 0.794541319181198} | {'accuracy': 0.7832} |
| 0.1314 | 107.0 | 749 | 1.2050 | {'f1': 0.7917448405253283} | {'accuracy': 0.778} |
| 0.1314 | 108.0 | 756 | 1.2009 | {'f1': 0.7927656367746798} | {'accuracy': 0.78} |
| 0.1314 | 109.0 | 763 | 1.1883 | {'f1': 0.7961904761904762} | {'accuracy': 0.786} |
| 0.1314 | 110.0 | 770 | 1.1846 | {'f1': 0.7966231772831925} | {'accuracy': 0.788} |
| 0.1314 | 111.0 | 777 | 1.1764 | {'f1': 0.7963836477987422} | {'accuracy': 0.7928} |
| 0.1314 | 112.0 | 784 | 1.1790 | {'f1': 0.7925129430505774} | {'accuracy': 0.7916} |
| 0.1314 | 113.0 | 791 | 1.1812 | {'f1': 0.7912175648702595} | {'accuracy': 0.7908} |
| 0.1314 | 114.0 | 798 | 1.1818 | {'f1': 0.7946074544012688} | {'accuracy': 0.7928} |
| 0.1314 | 115.0 | 805 | 1.2656 | {'f1': 0.7910883856829803} | {'accuracy': 0.7712} |
| 0.1314 | 116.0 | 812 | 1.3465 | {'f1': 0.7908799429996437} | {'accuracy': 0.7652} |
| 0.1314 | 117.0 | 819 | 1.3676 | {'f1': 0.7467619472979008} | {'accuracy': 0.7732} |
| 0.1314 | 118.0 | 826 | 1.1959 | {'f1': 0.793343653250774} | {'accuracy': 0.7864} |
| 0.1314 | 119.0 | 833 | 1.7826 | {'f1': 0.7632348164988633} | {'accuracy': 0.7084} |
| 0.1314 | 120.0 | 840 | 1.3750 | {'f1': 0.7883315546069015} | {'accuracy': 0.762} |
| 0.1314 | 121.0 | 847 | 1.3752 | {'f1': 0.7448397013614404} | {'accuracy': 0.7676} |
| 0.1314 | 122.0 | 854 | 1.3570 | {'f1': 0.7528187337380745} | {'accuracy': 0.772} |
| 0.1314 | 123.0 | 861 | 1.2615 | {'f1': 0.7824412783981518} | {'accuracy': 0.774} |
| 0.1314 | 124.0 | 868 | 1.4186 | {'f1': 0.784062611170402} | {'accuracy': 0.7572} |
| 0.1314 | 125.0 | 875 | 1.3408 | {'f1': 0.7841409691629957} | {'accuracy': 0.7648} |
| 0.1314 | 126.0 | 882 | 1.2820 | {'f1': 0.7837014470677838} | {'accuracy': 0.7728} |
| 0.1314 | 127.0 | 889 | 1.2712 | {'f1': 0.7812133072407045} | {'accuracy': 0.7764} |
| 0.1314 | 128.0 | 896 | 1.2721 | {'f1': 0.7791898332009531} | {'accuracy': 0.7776} |
| 0.1314 | 129.0 | 903 | 1.2767 | {'f1': 0.7793880837359098} | {'accuracy': 0.7808} |
| 0.1314 | 130.0 | 910 | 1.2776 | {'f1': 0.7798792756539236} | {'accuracy': 0.7812} |
| 0.1314 | 131.0 | 917 | 1.2761 | {'f1': 0.7800399201596806} | {'accuracy': 0.7796} |
| 0.1314 | 132.0 | 924 | 1.2763 | {'f1': 0.7783955520254171} | {'accuracy': 0.7768} |
| 0.1314 | 133.0 | 931 | 1.2775 | {'f1': 0.7808326787117046} | {'accuracy': 0.7768} |
| 0.1314 | 134.0 | 938 | 1.2832 | {'f1': 0.783113865220759} | {'accuracy': 0.776} |
| 0.1314 | 135.0 | 945 | 1.2875 | {'f1': 0.7847516365036581} | {'accuracy': 0.7764} |
| 0.1314 | 136.0 | 952 | 1.2848 | {'f1': 0.783113865220759} | {'accuracy': 0.776} |
| 0.1314 | 137.0 | 959 | 1.2817 | {'f1': 0.7817614964925955} | {'accuracy': 0.776} |
| 0.1314 | 138.0 | 966 | 1.2795 | {'f1': 0.782472613458529} | {'accuracy': 0.7776} |
| 0.1314 | 139.0 | 973 | 1.2813 | {'f1': 0.7837943124269575} | {'accuracy': 0.778} |
| 0.1314 | 140.0 | 980 | 1.2845 | {'f1': 0.7849670670282836} | {'accuracy': 0.778} |
| 0.1314 | 141.0 | 987 | 1.2940 | {'f1': 0.7870476190476191} | {'accuracy': 0.7764} |
| 0.1314 | 142.0 | 994 | 1.3119 | {'f1': 0.7866014301844184} | {'accuracy': 0.7732} |
| 0.0011 | 143.0 | 1001 | 1.3219 | {'f1': 0.7857677902621724} | {'accuracy': 0.7712} |
| 0.0011 | 144.0 | 1008 | 1.3216 | {'f1': 0.7860621955788686} | {'accuracy': 0.7716} |
| 0.0011 | 145.0 | 1015 | 1.3180 | {'f1': 0.7864661654135338} | {'accuracy': 0.7728} |
| 0.0011 | 146.0 | 1022 | 1.2972 | {'f1': 0.7888507063764795} | {'accuracy': 0.7788} |
| 0.0011 | 147.0 | 1029 | 1.2832 | {'f1': 0.7831893165750198} | {'accuracy': 0.7792} |
| 0.0011 | 148.0 | 1036 | 1.2828 | {'f1': 0.7818471337579618} | {'accuracy': 0.7808} |
| 0.0011 | 149.0 | 1043 | 1.2832 | {'f1': 0.7818471337579618} | {'accuracy': 0.7808} |
| 0.0011 | 150.0 | 1050 | 1.2842 | {'f1': 0.7844827586206896} | {'accuracy': 0.78} |
| 0.0011 | 151.0 | 1057 | 1.2883 | {'f1': 0.7882307394502517} | {'accuracy': 0.7812} |
| 0.0011 | 152.0 | 1064 | 1.2936 | {'f1': 0.7872258560984994} | {'accuracy': 0.7788} |
| 0.0011 | 153.0 | 1071 | 1.2996 | {'f1': 0.7885496183206105} | {'accuracy': 0.7784} |
| 0.0011 | 154.0 | 1078 | 1.3017 | {'f1': 0.7890118275467379} | {'accuracy': 0.7788} |
| 0.0011 | 155.0 | 1085 | 1.3029 | {'f1': 0.7890118275467379} | {'accuracy': 0.7788} |
| 0.0011 | 156.0 | 1092 | 1.3046 | {'f1': 0.7896341463414634} | {'accuracy': 0.7792} |
| 0.0011 | 157.0 | 1099 | 1.3024 | {'f1': 0.7894535727932748} | {'accuracy': 0.7796} |
| 0.0011 | 158.0 | 1106 | 1.3025 | {'f1': 0.7895944912012243} | {'accuracy': 0.78} |
| 0.0011 | 159.0 | 1113 | 1.3031 | {'f1': 0.7895944912012243} | {'accuracy': 0.78} |
| 0.0011 | 160.0 | 1120 | 1.3056 | {'f1': 0.7902364607170099} | {'accuracy': 0.78} |
| 0.0011 | 161.0 | 1127 | 1.3088 | {'f1': 0.7913340935005702} | {'accuracy': 0.7804} |
| 0.0011 | 162.0 | 1134 | 1.3108 | {'f1': 0.7904328018223236} | {'accuracy': 0.7792} |
| 0.0011 | 163.0 | 1141 | 1.3081 | {'f1': 0.7902364607170099} | {'accuracy': 0.78} |
| 0.0011 | 164.0 | 1148 | 1.3071 | {'f1': 0.7888507063764795} | {'accuracy': 0.7788} |
| 0.0011 | 165.0 | 1155 | 1.3060 | {'f1': 0.7891104294478529} | {'accuracy': 0.78} |
| 0.0011 | 166.0 | 1162 | 1.3071 | {'f1': 0.7888079724032196} | {'accuracy': 0.7796} |
| 0.0011 | 167.0 | 1169 | 1.3099 | {'f1': 0.7888507063764795} | {'accuracy': 0.7788} |
| 0.0011 | 168.0 | 1176 | 1.3124 | {'f1': 0.7897748950782144} | {'accuracy': 0.7796} |
| 0.0011 | 169.0 | 1183 | 1.3143 | {'f1': 0.791158536585366} | {'accuracy': 0.7808} |
| 0.0011 | 170.0 | 1190 | 1.3156 | {'f1': 0.7905559786747907} | {'accuracy': 0.78} |
| 0.0011 | 171.0 | 1197 | 1.3233 | {'f1': 0.7895335608646189} | {'accuracy': 0.778} |
| 0.0011 | 172.0 | 1204 | 1.3404 | {'f1': 0.7885843034171987} | {'accuracy': 0.7748} |
| 0.0011 | 173.0 | 1211 | 1.3476 | {'f1': 0.789689951438177} | {'accuracy': 0.7748} |
| 0.0011 | 174.0 | 1218 | 1.3488 | {'f1': 0.7717842323651453} | {'accuracy': 0.78} |
| 0.0011 | 175.0 | 1225 | 1.5156 | {'f1': 0.7329469460543914} | {'accuracy': 0.7604} |
| 0.0011 | 176.0 | 1232 | 1.4279 | {'f1': 0.7597097737942808} | {'accuracy': 0.7748} |
| 0.0011 | 177.0 | 1239 | 1.3662 | {'f1': 0.7785493827160495} | {'accuracy': 0.7704} |
| 0.0011 | 178.0 | 1246 | 1.4237 | {'f1': 0.7834135505368381} | {'accuracy': 0.766} |
| 0.0011 | 179.0 | 1253 | 1.4623 | {'f1': 0.7840991976659373} | {'accuracy': 0.7632} |
| 0.0011 | 180.0 | 1260 | 1.4648 | {'f1': 0.7838133430550492} | {'accuracy': 0.7628} |
| 0.0011 | 181.0 | 1267 | 1.4449 | {'f1': 0.7849779086892489} | {'accuracy': 0.7664} |
| 0.0011 | 182.0 | 1274 | 1.4326 | {'f1': 0.7832530566876621} | {'accuracy': 0.766} |
| 0.0011 | 183.0 | 1281 | 1.4211 | {'f1': 0.7855007473841554} | {'accuracy': 0.7704} |
| 0.0011 | 184.0 | 1288 | 1.4065 | {'f1': 0.7847719562759141} | {'accuracy': 0.7716} |
| 0.0011 | 185.0 | 1295 | 1.3939 | {'f1': 0.7822458270106222} | {'accuracy': 0.7704} |
| 0.0011 | 186.0 | 1302 | 1.3858 | {'f1': 0.782375478927203} | {'accuracy': 0.7728} |
| 0.0011 | 187.0 | 1309 | 1.3835 | {'f1': 0.7807692307692309} | {'accuracy': 0.772} |
| 0.0011 | 188.0 | 1316 | 1.3836 | {'f1': 0.7813702848344881} | {'accuracy': 0.7728} |
| 0.0011 | 189.0 | 1323 | 1.3812 | {'f1': 0.7786790266512167} | {'accuracy': 0.7708} |
| 0.0011 | 190.0 | 1330 | 1.3815 | {'f1': 0.7805630543771693} | {'accuracy': 0.7724} |
| 0.0011 | 191.0 | 1337 | 1.3819 | {'f1': 0.7810331534309947} | {'accuracy': 0.7728} |
| 0.0011 | 192.0 | 1344 | 1.3820 | {'f1': 0.7810331534309947} | {'accuracy': 0.7728} |
| 0.0011 | 193.0 | 1351 | 1.3816 | {'f1': 0.7805630543771693} | {'accuracy': 0.7724} |
| 0.0011 | 194.0 | 1358 | 1.3819 | {'f1': 0.7810331534309947} | {'accuracy': 0.7728} |
| 0.0011 | 195.0 | 1365 | 1.3824 | {'f1': 0.7810331534309947} | {'accuracy': 0.7728} |
| 0.0011 | 196.0 | 1372 | 1.3828 | {'f1': 0.7810331534309947} | {'accuracy': 0.7728} |
| 0.0011 | 197.0 | 1379 | 1.3831 | {'f1': 0.7815028901734103} | {'accuracy': 0.7732} |
| 0.0011 | 198.0 | 1386 | 1.3838 | {'f1': 0.7815028901734103} | {'accuracy': 0.7732} |
| 0.0011 | 199.0 | 1393 | 1.3842 | {'f1': 0.7815028901734103} | {'accuracy': 0.7732} |
| 0.0011 | 200.0 | 1400 | 1.3841 | {'f1': 0.7799227799227799} | {'accuracy': 0.772} |
| 0.0011 | 201.0 | 1407 | 1.3854 | {'f1': 0.7815028901734103} | {'accuracy': 0.7732} |
| 0.0011 | 202.0 | 1414 | 1.3869 | {'f1': 0.7824412783981518} | {'accuracy': 0.774} |
| 0.0011 | 203.0 | 1421 | 1.3891 | {'f1': 0.7821744141375336} | {'accuracy': 0.7732} |
| 0.0011 | 204.0 | 1428 | 1.3911 | {'f1': 0.7822085889570553} | {'accuracy': 0.7728} |
| 0.0011 | 205.0 | 1435 | 1.3919 | {'f1': 0.7828418230563002} | {'accuracy': 0.7732} |
| 0.0011 | 206.0 | 1442 | 1.3930 | {'f1': 0.7833078101071975} | {'accuracy': 0.7736} |
| 0.0011 | 207.0 | 1449 | 1.3940 | {'f1': 0.784238714613619} | {'accuracy': 0.7744} |
| 0.0011 | 208.0 | 1456 | 1.3946 | {'f1': 0.7839388145315488} | {'accuracy': 0.774} |
| 0.0011 | 209.0 | 1463 | 1.3936 | {'f1': 0.7833078101071975} | {'accuracy': 0.7736} |
| 0.0011 | 210.0 | 1470 | 1.3952 | {'f1': 0.784868169659916} | {'accuracy': 0.7748} |
| 0.0011 | 211.0 | 1477 | 1.3972 | {'f1': 0.7844334223578787} | {'accuracy': 0.774} |
| 0.0011 | 212.0 | 1484 | 1.3983 | {'f1': 0.7847619047619048} | {'accuracy': 0.774} |
| 0.0011 | 213.0 | 1491 | 1.3995 | {'f1': 0.7847619047619048} | {'accuracy': 0.774} |
| 0.0011 | 214.0 | 1498 | 1.4000 | {'f1': 0.7852246763137853} | {'accuracy': 0.7744} |
| 0.0001 | 215.0 | 1505 | 1.4006 | {'f1': 0.7852246763137853} | {'accuracy': 0.7744} |
| 0.0001 | 216.0 | 1512 | 1.4018 | {'f1': 0.7858501331304678} | {'accuracy': 0.7748} |
| 0.0001 | 217.0 | 1519 | 1.3886 | {'f1': 0.7771381578947368} | {'accuracy': 0.7832} |
| 0.0001 | 218.0 | 1526 | 1.4134 | {'f1': 0.7666243117323168} | {'accuracy': 0.7796} |
| 0.0001 | 219.0 | 1533 | 1.3959 | {'f1': 0.7732435843500212} | {'accuracy': 0.7844} |
| 0.0001 | 220.0 | 1540 | 1.3611 | {'f1': 0.7795632468067573} | {'accuracy': 0.786} |
| 0.0001 | 221.0 | 1547 | 1.3446 | {'f1': 0.7798946088366437} | {'accuracy': 0.7828} |
| 0.0001 | 222.0 | 1554 | 1.3378 | {'f1': 0.7845786963434022} | {'accuracy': 0.7832} |
| 0.0001 | 223.0 | 1561 | 1.3382 | {'f1': 0.7891637220259128} | {'accuracy': 0.7852} |
| 0.0001 | 224.0 | 1568 | 1.3406 | {'f1': 0.7892271662763466} | {'accuracy': 0.784} |
| 0.0001 | 225.0 | 1575 | 1.3434 | {'f1': 0.7894327894327895} | {'accuracy': 0.7832} |
| 0.0001 | 226.0 | 1582 | 1.3461 | {'f1': 0.7894736842105263} | {'accuracy': 0.7824} |
| 0.0001 | 227.0 | 1589 | 1.3489 | {'f1': 0.7901234567901235} | {'accuracy': 0.7824} |
| 0.0001 | 228.0 | 1596 | 1.3511 | {'f1': 0.79} | {'accuracy': 0.7816} |
| 0.0001 | 229.0 | 1603 | 1.3536 | {'f1': 0.7910906298003072} | {'accuracy': 0.7824} |
| 0.0001 | 230.0 | 1610 | 1.3557 | {'f1': 0.7900383141762451} | {'accuracy': 0.7808} |
| 0.0001 | 231.0 | 1617 | 1.3575 | {'f1': 0.7901990811638592} | {'accuracy': 0.7808} |
| 0.0001 | 232.0 | 1624 | 1.3600 | {'f1': 0.7916030534351144} | {'accuracy': 0.7816} |
| 0.0001 | 233.0 | 1631 | 1.3606 | {'f1': 0.792064097672644} | {'accuracy': 0.782} |
| 0.0001 | 234.0 | 1638 | 1.3590 | {'f1': 0.7901990811638592} | {'accuracy': 0.7808} |
| 0.0001 | 235.0 | 1645 | 1.3640 | {'f1': 0.7908571428571428} | {'accuracy': 0.7804} |
| 0.0001 | 236.0 | 1652 | 1.4906 | {'f1': 0.7496700395952485} | {'accuracy': 0.7724} |
| 0.0001 | 237.0 | 1659 | 1.4214 | {'f1': 0.763745704467354} | {'accuracy': 0.78} |
| 0.0001 | 238.0 | 1666 | 1.3359 | {'f1': 0.79293123319247} | {'accuracy': 0.7844} |
| 0.0001 | 239.0 | 1673 | 1.4193 | {'f1': 0.7922457937088515} | {'accuracy': 0.7728} |
| 0.0001 | 240.0 | 1680 | 1.3986 | {'f1': 0.7937800814513144} | {'accuracy': 0.7772} |
| 0.0001 | 241.0 | 1687 | 1.3507 | {'f1': 0.7939814814814814} | {'accuracy': 0.7864} |
| 0.0001 | 242.0 | 1694 | 1.3463 | {'f1': 0.7911915060951632} | {'accuracy': 0.7876} |
| 0.0001 | 243.0 | 1701 | 1.3898 | {'f1': 0.770649895178197} | {'accuracy': 0.7812} |
| 0.0001 | 244.0 | 1708 | 1.5725 | {'f1': 0.7329417080885677} | {'accuracy': 0.7636} |
| 0.0001 | 245.0 | 1715 | 1.4878 | {'f1': 0.7962630255120373} | {'accuracy': 0.7732} |
| 0.0001 | 246.0 | 1722 | 1.7124 | {'f1': 0.784688995215311} | {'accuracy': 0.748} |
| 0.0001 | 247.0 | 1729 | 1.6537 | {'f1': 0.7892190739460953} | {'accuracy': 0.756} |
| 0.0001 | 248.0 | 1736 | 1.4905 | {'f1': 0.7985585585585585} | {'accuracy': 0.7764} |
| 0.0001 | 249.0 | 1743 | 1.4141 | {'f1': 0.7965722801788375} | {'accuracy': 0.7816} |
| 0.0001 | 250.0 | 1750 | 1.3872 | {'f1': 0.7936387731919727} | {'accuracy': 0.782} |
| 0.0001 | 251.0 | 1757 | 1.3772 | {'f1': 0.7917620137299771} | {'accuracy': 0.7816} |
| 0.0001 | 252.0 | 1764 | 1.3735 | {'f1': 0.7914274779946421} | {'accuracy': 0.782} |
| 0.0001 | 253.0 | 1771 | 1.3733 | {'f1': 0.7908045977011494} | {'accuracy': 0.7816} |
| 0.0001 | 254.0 | 1778 | 1.3737 | {'f1': 0.790341126868532} | {'accuracy': 0.7812} |
| 0.0001 | 255.0 | 1785 | 1.3743 | {'f1': 0.790341126868532} | {'accuracy': 0.7812} |
| 0.0001 | 256.0 | 1792 | 1.3745 | {'f1': 0.790341126868532} | {'accuracy': 0.7812} |
| 0.0001 | 257.0 | 1799 | 1.3748 | {'f1': 0.790341126868532} | {'accuracy': 0.7812} |
| 0.0001 | 258.0 | 1806 | 1.4268 | {'f1': 0.7959791511541325} | {'accuracy': 0.7808} |
| 0.0001 | 259.0 | 1813 | 1.5076 | {'f1': 0.7963898916967509} | {'accuracy': 0.7744} |
| 0.0001 | 260.0 | 1820 | 1.5107 | {'f1': 0.7939372067845544} | {'accuracy': 0.7716} |
| 0.0001 | 261.0 | 1827 | 1.4823 | {'f1': 0.7975236707938821} | {'accuracy': 0.7776} |
| 0.0001 | 262.0 | 1834 | 1.4623 | {'f1': 0.7969151670951157} | {'accuracy': 0.7788} |
| 0.0001 | 263.0 | 1841 | 1.4505 | {'f1': 0.7948243992606286} | {'accuracy': 0.778} |
| 0.0001 | 264.0 | 1848 | 1.4385 | {'f1': 0.7959866220735785} | {'accuracy': 0.7804} |
| 0.0001 | 265.0 | 1855 | 1.4309 | {'f1': 0.7955223880597015} | {'accuracy': 0.7808} |
| 0.0001 | 266.0 | 1862 | 1.4251 | {'f1': 0.7934008248968879} | {'accuracy': 0.7796} |
| 0.0001 | 267.0 | 1869 | 1.4218 | {'f1': 0.7921833897031192} | {'accuracy': 0.7788} |
| 0.0001 | 268.0 | 1876 | 1.4193 | {'f1': 0.79155672823219} | {'accuracy': 0.7788} |
| 0.0001 | 269.0 | 1883 | 1.4154 | {'f1': 0.7924528301886793} | {'accuracy': 0.78} |
| 0.0001 | 270.0 | 1890 | 1.4131 | {'f1': 0.7922960725075529} | {'accuracy': 0.78} |
| 0.0001 | 271.0 | 1897 | 1.4115 | {'f1': 0.7928949357520786} | {'accuracy': 0.7808} |
| 0.0001 | 272.0 | 1904 | 1.4094 | {'f1': 0.791981845688351} | {'accuracy': 0.78} |
| 0.0001 | 273.0 | 1911 | 1.4077 | {'f1': 0.7924242424242425} | {'accuracy': 0.7808} |
| 0.0001 | 274.0 | 1918 | 1.4073 | {'f1': 0.7924242424242425} | {'accuracy': 0.7808} |
| 0.0001 | 275.0 | 1925 | 1.4076 | {'f1': 0.7924242424242425} | {'accuracy': 0.7808} |
| 0.0001 | 276.0 | 1932 | 1.4057 | {'f1': 0.7933257489571482} | {'accuracy': 0.782} |
| 0.0001 | 277.0 | 1939 | 1.4048 | {'f1': 0.7937713634637297} | {'accuracy': 0.7828} |
| 0.0001 | 278.0 | 1946 | 1.4038 | {'f1': 0.7937713634637297} | {'accuracy': 0.7828} |
| 0.0001 | 279.0 | 1953 | 1.4018 | {'f1': 0.7929984779299847} | {'accuracy': 0.7824} |
| 0.0001 | 280.0 | 1960 | 1.3990 | {'f1': 0.7932875667429442} | {'accuracy': 0.7832} |
| 0.0001 | 281.0 | 1967 | 1.3975 | {'f1': 0.7929717341482048} | {'accuracy': 0.7832} |
| 0.0001 | 282.0 | 1974 | 1.3984 | {'f1': 0.7926689576174113} | {'accuracy': 0.7828} |
| 0.0001 | 283.0 | 1981 | 1.4018 | {'f1': 0.7931428571428571} | {'accuracy': 0.7828} |
| 0.0001 | 284.0 | 1988 | 1.4044 | {'f1': 0.7926968429060479} | {'accuracy': 0.782} |
| 0.0001 | 285.0 | 1995 | 1.4058 | {'f1': 0.7923954372623574} | {'accuracy': 0.7816} |
| 0.0007 | 286.0 | 2002 | 1.4061 | {'f1': 0.7923954372623574} | {'accuracy': 0.7816} |
| 0.0007 | 287.0 | 2009 | 1.4036 | {'f1': 0.7933003425961174} | {'accuracy': 0.7828} |
| 0.0007 | 288.0 | 2016 | 1.4021 | {'f1': 0.7932875667429442} | {'accuracy': 0.7832} |
| 0.0007 | 289.0 | 2023 | 1.4016 | {'f1': 0.7932875667429442} | {'accuracy': 0.7832} |
| 0.0007 | 290.0 | 2030 | 1.4016 | {'f1': 0.7928271652041207} | {'accuracy': 0.7828} |
| 0.0007 | 291.0 | 2037 | 1.4012 | {'f1': 0.7929717341482048} | {'accuracy': 0.7832} |
| 0.0007 | 292.0 | 2044 | 1.4013 | {'f1': 0.7929717341482048} | {'accuracy': 0.7832} |
| 0.0007 | 293.0 | 2051 | 1.4023 | {'f1': 0.7928271652041207} | {'accuracy': 0.7828} |
| 0.0007 | 294.0 | 2058 | 1.4030 | {'f1': 0.7928271652041207} | {'accuracy': 0.7828} |
| 0.0007 | 295.0 | 2065 | 1.4028 | {'f1': 0.7923664122137405} | {'accuracy': 0.7824} |
| 0.0007 | 296.0 | 2072 | 1.4024 | {'f1': 0.7929717341482048} | {'accuracy': 0.7832} |
| 0.0007 | 297.0 | 2079 | 1.4017 | {'f1': 0.7920489296636085} | {'accuracy': 0.7824} |
| 0.0007 | 298.0 | 2086 | 1.4032 | {'f1': 0.7926689576174113} | {'accuracy': 0.7828} |
| 0.0007 | 299.0 | 2093 | 1.4049 | {'f1': 0.7932875667429442} | {'accuracy': 0.7832} |
| 0.0007 | 300.0 | 2100 | 1.4070 | {'f1': 0.7942073170731707} | {'accuracy': 0.784} |
| 0.0007 | 301.0 | 2107 | 1.4084 | {'f1': 0.7933003425961174} | {'accuracy': 0.7828} |
| 0.0007 | 302.0 | 2114 | 1.4103 | {'f1': 0.7929984779299847} | {'accuracy': 0.7824} |
| 0.0007 | 303.0 | 2121 | 1.4113 | {'f1': 0.7926968429060479} | {'accuracy': 0.782} |
| 0.0007 | 304.0 | 2128 | 1.4121 | {'f1': 0.7931558935361217} | {'accuracy': 0.7824} |
| 0.0007 | 305.0 | 2135 | 1.4132 | {'f1': 0.7933130699088147} | {'accuracy': 0.7824} |
| 0.0007 | 306.0 | 2142 | 1.4138 | {'f1': 0.7933130699088147} | {'accuracy': 0.7824} |
| 0.0007 | 307.0 | 2149 | 1.4149 | {'f1': 0.7934700075930143} | {'accuracy': 0.7824} |
| 0.0007 | 308.0 | 2156 | 1.4146 | {'f1': 0.7933130699088147} | {'accuracy': 0.7824} |
| 0.0007 | 309.0 | 2163 | 1.4145 | {'f1': 0.7933130699088147} | {'accuracy': 0.7824} |
| 0.0007 | 310.0 | 2170 | 1.4145 | {'f1': 0.7931558935361217} | {'accuracy': 0.7824} |
| 0.0007 | 311.0 | 2177 | 1.4146 | {'f1': 0.7931558935361217} | {'accuracy': 0.7824} |
| 0.0007 | 312.0 | 2184 | 1.4136 | {'f1': 0.7926968429060479} | {'accuracy': 0.782} |
| 0.0007 | 313.0 | 2191 | 1.4138 | {'f1': 0.7929984779299847} | {'accuracy': 0.7824} |
| 0.0007 | 314.0 | 2198 | 1.4132 | {'f1': 0.7933003425961174} | {'accuracy': 0.7828} |
| 0.0007 | 315.0 | 2205 | 1.3997 | {'f1': 0.7903039630627166} | {'accuracy': 0.782} |
| 0.0007 | 316.0 | 2212 | 1.3935 | {'f1': 0.7934908950019373} | {'accuracy': 0.7868} |
| 0.0007 | 317.0 | 2219 | 1.3914 | {'f1': 0.79423226812159} | {'accuracy': 0.7888} |
| 0.0007 | 318.0 | 2226 | 1.3912 | {'f1': 0.7943815840811549} | {'accuracy': 0.7892} |
| 0.0007 | 319.0 | 2233 | 1.3916 | {'f1': 0.7943815840811549} | {'accuracy': 0.7892} |
| 0.0007 | 320.0 | 2240 | 1.3921 | {'f1': 0.7940717628705148} | {'accuracy': 0.7888} |
| 0.0007 | 321.0 | 2247 | 1.3930 | {'f1': 0.79423226812159} | {'accuracy': 0.7888} |
| 0.0007 | 322.0 | 2254 | 1.3940 | {'f1': 0.7948618139353834} | {'accuracy': 0.7892} |
| 0.0007 | 323.0 | 2261 | 1.3951 | {'f1': 0.7939464493597206} | {'accuracy': 0.7876} |
| 0.0007 | 324.0 | 2268 | 1.3963 | {'f1': 0.7937984496124032} | {'accuracy': 0.7872} |
| 0.0007 | 325.0 | 2275 | 1.3971 | {'f1': 0.7939581719597211} | {'accuracy': 0.7872} |
| 0.0007 | 326.0 | 2282 | 1.3976 | {'f1': 0.7939581719597211} | {'accuracy': 0.7872} |
| 0.0007 | 327.0 | 2289 | 1.3986 | {'f1': 0.7927300850734724} | {'accuracy': 0.7856} |
| 0.0007 | 328.0 | 2296 | 1.3996 | {'f1': 0.7924236567452647} | {'accuracy': 0.7852} |
| 0.0007 | 329.0 | 2303 | 1.4009 | {'f1': 0.7918115102356122} | {'accuracy': 0.7844} |
| 0.0007 | 330.0 | 2310 | 1.3936 | {'f1': 0.7940717628705148} | {'accuracy': 0.7888} |
| 0.0007 | 331.0 | 2317 | 1.3906 | {'f1': 0.7943262411347518} | {'accuracy': 0.7912} |
| 0.0007 | 332.0 | 2324 | 1.4082 | {'f1': 0.7843942505133472} | {'accuracy': 0.79} |
| 0.0007 | 333.0 | 2331 | 1.4965 | {'f1': 0.7654427645788336} | {'accuracy': 0.7828} |
| 0.0007 | 334.0 | 2338 | 1.4472 | {'f1': 0.7962756052141526} | {'accuracy': 0.7812} |
| 0.0007 | 335.0 | 2345 | 1.8246 | {'f1': 0.7844594594594595} | {'accuracy': 0.7448} |
| 0.0007 | 336.0 | 2352 | 1.4729 | {'f1': 0.7955637707948244} | {'accuracy': 0.7788} |
| 0.0007 | 337.0 | 2359 | 1.4028 | {'f1': 0.7921662669864109} | {'accuracy': 0.792} |
| 0.0007 | 338.0 | 2366 | 1.4316 | {'f1': 0.7816377171215881} | {'accuracy': 0.7888} |
| 0.0007 | 339.0 | 2373 | 1.4526 | {'f1': 0.776006711409396} | {'accuracy': 0.7864} |
| 0.0007 | 340.0 | 2380 | 1.4467 | {'f1': 0.7761567319716548} | {'accuracy': 0.7852} |
| 0.0007 | 341.0 | 2387 | 1.4355 | {'f1': 0.7806291390728477} | {'accuracy': 0.788} |
| 0.0007 | 342.0 | 2394 | 1.4260 | {'f1': 0.7837505129257283} | {'accuracy': 0.7892} |
| 0.0007 | 343.0 | 2401 | 1.4213 | {'f1': 0.7861224489795918} | {'accuracy': 0.7904} |
| 0.0007 | 344.0 | 2408 | 1.4110 | {'f1': 0.7934609250398725} | {'accuracy': 0.7928} |
| 0.0007 | 345.0 | 2415 | 1.4141 | {'f1': 0.7943485086342229} | {'accuracy': 0.7904} |
| 0.0007 | 346.0 | 2422 | 1.4232 | {'f1': 0.7927300850734724} | {'accuracy': 0.7856} |
| 0.0007 | 347.0 | 2429 | 1.4409 | {'f1': 0.7940841865756542} | {'accuracy': 0.7828} |
| 0.0007 | 348.0 | 2436 | 1.4519 | {'f1': 0.7929083364768013} | {'accuracy': 0.7804} |
| 0.0007 | 349.0 | 2443 | 1.4572 | {'f1': 0.7923394667668042} | {'accuracy': 0.7788} |
| 0.0007 | 350.0 | 2450 | 1.4591 | {'f1': 0.7929482370592649} | {'accuracy': 0.7792} |
| 0.0007 | 351.0 | 2457 | 1.4579 | {'f1': 0.7923394667668042} | {'accuracy': 0.7788} |
| 0.0007 | 352.0 | 2464 | 1.4567 | {'f1': 0.7926232593150169} | {'accuracy': 0.7796} |
| 0.0007 | 353.0 | 2471 | 1.4555 | {'f1': 0.7927656367746798} | {'accuracy': 0.78} |
| 0.0007 | 354.0 | 2478 | 1.4544 | {'f1': 0.7930644553335846} | {'accuracy': 0.7804} |
| 0.0007 | 355.0 | 2485 | 1.4542 | {'f1': 0.7926093514328808} | {'accuracy': 0.78} |
| 0.0007 | 356.0 | 2492 | 1.4542 | {'f1': 0.7926093514328808} | {'accuracy': 0.78} |
| 0.0007 | 357.0 | 2499 | 1.4540 | {'f1': 0.7926093514328808} | {'accuracy': 0.78} |
| 0.0007 | 358.0 | 2506 | 1.4528 | {'f1': 0.7929083364768013} | {'accuracy': 0.7804} |
| 0.0007 | 359.0 | 2513 | 1.4517 | {'f1': 0.7935069837674594} | {'accuracy': 0.7812} |
| 0.0007 | 360.0 | 2520 | 1.4514 | {'f1': 0.7930513595166163} | {'accuracy': 0.7808} |
| 0.0007 | 361.0 | 2527 | 1.4512 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 362.0 | 2534 | 1.4511 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 363.0 | 2541 | 1.4511 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 364.0 | 2548 | 1.4509 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 365.0 | 2555 | 1.4510 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 366.0 | 2562 | 1.4512 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 367.0 | 2569 | 1.4513 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 368.0 | 2576 | 1.4521 | {'f1': 0.7936507936507937} | {'accuracy': 0.7816} |
| 0.0007 | 369.0 | 2583 | 1.4525 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 370.0 | 2590 | 1.4527 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 371.0 | 2597 | 1.4526 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 372.0 | 2604 | 1.4527 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 373.0 | 2611 | 1.4523 | {'f1': 0.7931947069943289} | {'accuracy': 0.7812} |
| 0.0007 | 374.0 | 2618 | 1.4521 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.0007 | 375.0 | 2625 | 1.4521 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.0007 | 376.0 | 2632 | 1.4522 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.0007 | 377.0 | 2639 | 1.4522 | {'f1': 0.7937949300037835} | {'accuracy': 0.782} |
| 0.0007 | 378.0 | 2646 | 1.4500 | {'f1': 0.7936387731919727} | {'accuracy': 0.782} |
| 0.0007 | 379.0 | 2653 | 1.4484 | {'f1': 0.7943854324734447} | {'accuracy': 0.7832} |
| 0.0007 | 380.0 | 2660 | 1.4490 | {'f1': 0.7940841865756542} | {'accuracy': 0.7828} |
| 0.0007 | 381.0 | 2667 | 1.4501 | {'f1': 0.7937831690674755} | {'accuracy': 0.7824} |
| 0.0007 | 382.0 | 2674 | 1.4526 | {'f1': 0.7933383800151401} | {'accuracy': 0.7816} |
| 0.0007 | 383.0 | 2681 | 1.4539 | {'f1': 0.7937949300037835} | {'accuracy': 0.782} |
| 0.0007 | 384.0 | 2688 | 1.4547 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.0007 | 385.0 | 2695 | 1.4551 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.0007 | 386.0 | 2702 | 1.4548 | {'f1': 0.7934947049924357} | {'accuracy': 0.7816} |
| 0.0007 | 387.0 | 2709 | 1.4544 | {'f1': 0.7937949300037835} | {'accuracy': 0.782} |
| 0.0007 | 388.0 | 2716 | 1.4573 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 389.0 | 2723 | 1.4598 | {'f1': 0.7935069837674594} | {'accuracy': 0.7812} |
| 0.0007 | 390.0 | 2730 | 1.4606 | {'f1': 0.7935069837674594} | {'accuracy': 0.7812} |
| 0.0007 | 391.0 | 2737 | 1.4607 | {'f1': 0.7935069837674594} | {'accuracy': 0.7812} |
| 0.0007 | 392.0 | 2744 | 1.4618 | {'f1': 0.7929083364768013} | {'accuracy': 0.7804} |
| 0.0007 | 393.0 | 2751 | 1.4636 | {'f1': 0.7923105917828872} | {'accuracy': 0.7796} |
| 0.0007 | 394.0 | 2758 | 1.4640 | {'f1': 0.792467043314501} | {'accuracy': 0.7796} |
| 0.0007 | 395.0 | 2765 | 1.4590 | {'f1': 0.7933509633547412} | {'accuracy': 0.7812} |
| 0.0007 | 396.0 | 2772 | 1.4491 | {'f1': 0.796952380952381} | {'accuracy': 0.7868} |
| 0.0007 | 397.0 | 2779 | 1.4451 | {'f1': 0.7954022988505747} | {'accuracy': 0.7864} |
| 0.0007 | 398.0 | 2786 | 1.4437 | {'f1': 0.794783275795934} | {'accuracy': 0.786} |
| 0.0007 | 399.0 | 2793 | 1.4396 | {'f1': 0.7935285053929122} | {'accuracy': 0.7856} |
| 0.0007 | 400.0 | 2800 | 1.4382 | {'f1': 0.7916505604947817} | {'accuracy': 0.7844} |
| 0.0007 | 401.0 | 2807 | 1.4377 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 402.0 | 2814 | 1.4373 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 403.0 | 2821 | 1.4371 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 404.0 | 2828 | 1.4372 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 405.0 | 2835 | 1.4374 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 406.0 | 2842 | 1.4376 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 407.0 | 2849 | 1.4377 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 408.0 | 2856 | 1.4381 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 409.0 | 2863 | 1.4385 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 410.0 | 2870 | 1.4388 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 411.0 | 2877 | 1.4389 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 412.0 | 2884 | 1.4395 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 413.0 | 2891 | 1.4408 | {'f1': 0.7919722115013508} | {'accuracy': 0.7844} |
| 0.0007 | 414.0 | 2898 | 1.4419 | {'f1': 0.7938342967244703} | {'accuracy': 0.786} |
| 0.0007 | 415.0 | 2905 | 1.4401 | {'f1': 0.7919566898685229} | {'accuracy': 0.7848} |
| 0.0007 | 416.0 | 2912 | 1.4395 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 417.0 | 2919 | 1.4393 | {'f1': 0.7925696594427244} | {'accuracy': 0.7856} |
| 0.0007 | 418.0 | 2926 | 1.4393 | {'f1': 0.7925696594427244} | {'accuracy': 0.7856} |
| 0.0007 | 419.0 | 2933 | 1.4396 | {'f1': 0.7925696594427244} | {'accuracy': 0.7856} |
| 0.0007 | 420.0 | 2940 | 1.4398 | {'f1': 0.7925696594427244} | {'accuracy': 0.7856} |
| 0.0007 | 421.0 | 2947 | 1.4401 | {'f1': 0.7925696594427244} | {'accuracy': 0.7856} |
| 0.0007 | 422.0 | 2954 | 1.4404 | {'f1': 0.7922630560928433} | {'accuracy': 0.7852} |
| 0.0007 | 423.0 | 2961 | 1.4410 | {'f1': 0.7919566898685229} | {'accuracy': 0.7848} |
| 0.0007 | 424.0 | 2968 | 1.4415 | {'f1': 0.7919566898685229} | {'accuracy': 0.7848} |
| 0.0007 | 425.0 | 2975 | 1.4417 | {'f1': 0.7919566898685229} | {'accuracy': 0.7848} |
| 0.0007 | 426.0 | 2982 | 1.4419 | {'f1': 0.7919566898685229} | {'accuracy': 0.7848} |
| 0.0007 | 427.0 | 2989 | 1.4422 | {'f1': 0.7916505604947817} | {'accuracy': 0.7844} |
| 0.0007 | 428.0 | 2996 | 1.4425 | {'f1': 0.7921174652241112} | {'accuracy': 0.7848} |
| 0.0 | 429.0 | 3003 | 1.4428 | {'f1': 0.7921174652241112} | {'accuracy': 0.7848} |
| 0.0 | 430.0 | 3010 | 1.4430 | {'f1': 0.7921174652241112} | {'accuracy': 0.7848} |
| 0.0 | 431.0 | 3017 | 1.4432 | {'f1': 0.7925840092699883} | {'accuracy': 0.7852} |
| 0.0 | 432.0 | 3024 | 1.4434 | {'f1': 0.793050193050193} | {'accuracy': 0.7856} |
| 0.0 | 433.0 | 3031 | 1.4435 | {'f1': 0.793050193050193} | {'accuracy': 0.7856} |
| 0.0 | 434.0 | 3038 | 1.4435 | {'f1': 0.7925840092699883} | {'accuracy': 0.7852} |
| 0.0 | 435.0 | 3045 | 1.4412 | {'f1': 0.7903225806451615} | {'accuracy': 0.792} |
| 0.0 | 436.0 | 3052 | 1.5055 | {'f1': 0.7695560253699789} | {'accuracy': 0.782} |
| 0.0 | 437.0 | 3059 | 1.5542 | {'f1': 0.7638709677419356} | {'accuracy': 0.7804} |
| 0.0 | 438.0 | 3066 | 1.5717 | {'f1': 0.7592190889370932} | {'accuracy': 0.778} |
| 0.0 | 439.0 | 3073 | 1.5722 | {'f1': 0.7586805555555556} | {'accuracy': 0.7776} |
| 0.0 | 440.0 | 3080 | 1.5614 | {'f1': 0.7627264883520276} | {'accuracy': 0.78} |
| 0.0 | 441.0 | 3087 | 1.5519 | {'f1': 0.7642765135251182} | {'accuracy': 0.7804} |
| 0.0 | 442.0 | 3094 | 1.5332 | {'f1': 0.7669365146996164} | {'accuracy': 0.7812} |
| 0.0 | 443.0 | 3101 | 1.5032 | {'f1': 0.7716933445661331} | {'accuracy': 0.7832} |
| 0.0 | 444.0 | 3108 | 1.4862 | {'f1': 0.7756463719766471} | {'accuracy': 0.7848} |
| 0.0 | 445.0 | 3115 | 1.4768 | {'f1': 0.7759336099585062} | {'accuracy': 0.784} |
| 0.0 | 446.0 | 3122 | 1.4716 | {'f1': 0.77819083023544} | {'accuracy': 0.7852} |
| 0.0 | 447.0 | 3129 | 1.4686 | {'f1': 0.7778695293146161} | {'accuracy': 0.7848} |
| 0.0 | 448.0 | 3136 | 1.4661 | {'f1': 0.7782357790601815} | {'accuracy': 0.7848} |
| 0.0 | 449.0 | 3143 | 1.4639 | {'f1': 0.7829298317603612} | {'accuracy': 0.7884} |
| 0.0 | 450.0 | 3150 | 1.4625 | {'f1': 0.7824662023760756} | {'accuracy': 0.7876} |
| 0.0 | 451.0 | 3157 | 1.4611 | {'f1': 0.7828220858895706} | {'accuracy': 0.7876} |
| 0.0 | 452.0 | 3164 | 1.4597 | {'f1': 0.7834967320261439} | {'accuracy': 0.788} |
| 0.0 | 453.0 | 3171 | 1.4576 | {'f1': 0.7853360488798371} | {'accuracy': 0.7892} |
| 0.0 | 454.0 | 3178 | 1.4564 | {'f1': 0.7881493506493508} | {'accuracy': 0.7912} |
| 0.0 | 455.0 | 3185 | 1.4556 | {'f1': 0.7888123226591002} | {'accuracy': 0.7916} |
| 0.0 | 456.0 | 3192 | 1.4550 | {'f1': 0.7894736842105263} | {'accuracy': 0.792} |
| 0.0 | 457.0 | 3199 | 1.4543 | {'f1': 0.7894736842105263} | {'accuracy': 0.792} |
| 0.0 | 458.0 | 3206 | 1.4537 | {'f1': 0.7909421754953497} | {'accuracy': 0.7932} |
| 0.0 | 459.0 | 3213 | 1.4532 | {'f1': 0.7920872022607993} | {'accuracy': 0.794} |
| 0.0 | 460.0 | 3220 | 1.4524 | {'f1': 0.7919354838709676} | {'accuracy': 0.7936} |
| 0.0 | 461.0 | 3227 | 1.4515 | {'f1': 0.7914653784219002} | {'accuracy': 0.7928} |
| 0.0 | 462.0 | 3234 | 1.4510 | {'f1': 0.7918006430868169} | {'accuracy': 0.7928} |
| 0.0 | 463.0 | 3241 | 1.4507 | {'f1': 0.7924528301886792} | {'accuracy': 0.7932} |
| 0.0 | 464.0 | 3248 | 1.4506 | {'f1': 0.7921348314606741} | {'accuracy': 0.7928} |
| 0.0 | 465.0 | 3255 | 1.4504 | {'f1': 0.7931034482758622} | {'accuracy': 0.7936} |
| 0.0 | 466.0 | 3262 | 1.4502 | {'f1': 0.7931034482758622} | {'accuracy': 0.7936} |
| 0.0 | 467.0 | 3269 | 1.4501 | {'f1': 0.7929515418502203} | {'accuracy': 0.7932} |
| 0.0 | 468.0 | 3276 | 1.4499 | {'f1': 0.7931172468987595} | {'accuracy': 0.7932} |
| 0.0 | 469.0 | 3283 | 1.4498 | {'f1': 0.7936} | {'accuracy': 0.7936} |
| 0.0 | 470.0 | 3290 | 1.4497 | {'f1': 0.7937649880095924} | {'accuracy': 0.7936} |
| 0.0 | 471.0 | 3297 | 1.4497 | {'f1': 0.7937649880095924} | {'accuracy': 0.7936} |
| 0.0 | 472.0 | 3304 | 1.4495 | {'f1': 0.7937649880095924} | {'accuracy': 0.7936} |
| 0.0 | 473.0 | 3311 | 1.4494 | {'f1': 0.7936127744510977} | {'accuracy': 0.7932} |
| 0.0 | 474.0 | 3318 | 1.4486 | {'f1': 0.7937774232149979} | {'accuracy': 0.7932} |
| 0.0 | 475.0 | 3325 | 1.4479 | {'f1': 0.7931446791550419} | {'accuracy': 0.7924} |
| 0.0 | 476.0 | 3332 | 1.4476 | {'f1': 0.7937898089171975} | {'accuracy': 0.7928} |
| 0.0 | 477.0 | 3339 | 1.4475 | {'f1': 0.7934739355352168} | {'accuracy': 0.7924} |
| 0.0 | 478.0 | 3346 | 1.4475 | {'f1': 0.7934739355352168} | {'accuracy': 0.7924} |
| 0.0 | 479.0 | 3353 | 1.4475 | {'f1': 0.7931583134447096} | {'accuracy': 0.792} |
| 0.0 | 480.0 | 3360 | 1.4475 | {'f1': 0.793322734499205} | {'accuracy': 0.792} |
| 0.0 | 481.0 | 3367 | 1.4475 | {'f1': 0.7930075486690504} | {'accuracy': 0.7916} |
| 0.0 | 482.0 | 3374 | 1.4475 | {'f1': 0.7930075486690504} | {'accuracy': 0.7916} |
| 0.0 | 483.0 | 3381 | 1.4476 | {'f1': 0.7934868943606036} | {'accuracy': 0.792} |
| 0.0 | 484.0 | 3388 | 1.4476 | {'f1': 0.7934868943606036} | {'accuracy': 0.792} |
| 0.0 | 485.0 | 3395 | 1.4476 | {'f1': 0.7934868943606036} | {'accuracy': 0.792} |
| 0.0 | 486.0 | 3402 | 1.4477 | {'f1': 0.7931718936085749} | {'accuracy': 0.7916} |
| 0.0 | 487.0 | 3409 | 1.4477 | {'f1': 0.7931718936085749} | {'accuracy': 0.7916} |
| 0.0 | 488.0 | 3416 | 1.4477 | {'f1': 0.7925426418088061} | {'accuracy': 0.7908} |
| 0.0 | 489.0 | 3423 | 1.4478 | {'f1': 0.7925426418088061} | {'accuracy': 0.7908} |
| 0.0 | 490.0 | 3430 | 1.4478 | {'f1': 0.7925426418088061} | {'accuracy': 0.7908} |
| 0.0 | 491.0 | 3437 | 1.4478 | {'f1': 0.7930214115781127} | {'accuracy': 0.7912} |
| 0.0 | 492.0 | 3444 | 1.4478 | {'f1': 0.7930214115781127} | {'accuracy': 0.7912} |
| 0.0 | 493.0 | 3451 | 1.4479 | {'f1': 0.7930214115781127} | {'accuracy': 0.7912} |
| 0.0 | 494.0 | 3458 | 1.4483 | {'f1': 0.7941640378548895} | {'accuracy': 0.7912} |
| 0.0 | 495.0 | 3465 | 1.4496 | {'f1': 0.7930899096976836} | {'accuracy': 0.7892} |
| 0.0 | 496.0 | 3472 | 1.4504 | {'f1': 0.7935761848805327} | {'accuracy': 0.7892} |
| 0.0 | 497.0 | 3479 | 1.4507 | {'f1': 0.7951524628616107} | {'accuracy': 0.7904} |
| 0.0 | 498.0 | 3486 | 1.4508 | {'f1': 0.795623290347792} | {'accuracy': 0.7908} |
| 0.0 | 499.0 | 3493 | 1.4509 | {'f1': 0.795623290347792} | {'accuracy': 0.7908} |
| 0.0001 | 500.0 | 3500 | 1.4509 | {'f1': 0.795623290347792} | {'accuracy': 0.7908} |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
