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bert-base-uncased-test_16_500
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: 1.2555
- F1: {'f1': 0.8858187728565624}
- Accuracy: {'accuracy': 0.8876}
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 | 32 | 0.6772 | {'f1': 0.4197396963123644} | {'accuracy': 0.572} |
| No log | 2.0 | 64 | 0.6149 | {'f1': 0.6325088339222615} | {'accuracy': 0.6672} |
| No log | 3.0 | 96 | 0.4616 | {'f1': 0.7809983896940419} | {'accuracy': 0.7824} |
| No log | 4.0 | 128 | 0.4102 | {'f1': 0.837847866419295} | {'accuracy': 0.8252} |
| No log | 5.0 | 160 | 0.3876 | {'f1': 0.8469551282051282} | {'accuracy': 0.8472} |
| No log | 6.0 | 192 | 0.3908 | {'f1': 0.8325639949643308} | {'accuracy': 0.8404} |
| No log | 7.0 | 224 | 0.3592 | {'f1': 0.8642638036809817} | {'accuracy': 0.8584} |
| No log | 8.0 | 256 | 0.3725 | {'f1': 0.8675728155339806} | {'accuracy': 0.8636} |
| No log | 9.0 | 288 | 0.3765 | {'f1': 0.859775641025641} | {'accuracy': 0.86} |
| No log | 10.0 | 320 | 0.4279 | {'f1': 0.8678267308404295} | {'accuracy': 0.8572} |
| No log | 11.0 | 352 | 0.4188 | {'f1': 0.8585055643879174} | {'accuracy': 0.8576} |
| No log | 12.0 | 384 | 0.4489 | {'f1': 0.8583028826634186} | {'accuracy': 0.8604} |
| No log | 13.0 | 416 | 0.5263 | {'f1': 0.8507209499575912} | {'accuracy': 0.8592} |
| No log | 14.0 | 448 | 0.4985 | {'f1': 0.8688591149005278} | {'accuracy': 0.8708} |
| No log | 15.0 | 480 | 0.5142 | {'f1': 0.870710295291301} | {'accuracy': 0.8704} |
| 0.2771 | 16.0 | 512 | 0.5228 | {'f1': 0.8710325431900362} | {'accuracy': 0.8716} |
| 0.2771 | 17.0 | 544 | 0.5367 | {'f1': 0.87890625} | {'accuracy': 0.876} |
| 0.2771 | 18.0 | 576 | 0.5657 | {'f1': 0.8638420403126286} | {'accuracy': 0.8676} |
| 0.2771 | 19.0 | 608 | 0.6005 | {'f1': 0.8697588126159554} | {'accuracy': 0.8596} |
| 0.2771 | 20.0 | 640 | 0.6059 | {'f1': 0.8561900791996665} | {'accuracy': 0.862} |
| 0.2771 | 21.0 | 672 | 0.5729 | {'f1': 0.8786936236391913} | {'accuracy': 0.8752} |
| 0.2771 | 22.0 | 704 | 0.6494 | {'f1': 0.862111801242236} | {'accuracy': 0.8668} |
| 0.2771 | 23.0 | 736 | 0.6270 | {'f1': 0.8745490981963927} | {'accuracy': 0.8748} |
| 0.2771 | 24.0 | 768 | 0.6396 | {'f1': 0.8783521181500195} | {'accuracy': 0.8748} |
| 0.2771 | 25.0 | 800 | 0.6909 | {'f1': 0.8643379366368805} | {'accuracy': 0.8664} |
| 0.2771 | 26.0 | 832 | 0.7048 | {'f1': 0.8665048543689321} | {'accuracy': 0.868} |
| 0.2771 | 27.0 | 864 | 0.8026 | {'f1': 0.8516949152542372} | {'accuracy': 0.86} |
| 0.2771 | 28.0 | 896 | 0.7183 | {'f1': 0.8744448930157448} | {'accuracy': 0.8756} |
| 0.2771 | 29.0 | 928 | 0.7226 | {'f1': 0.8765581021310815} | {'accuracy': 0.8772} |
| 0.2771 | 30.0 | 960 | 0.7365 | {'f1': 0.8778930566640064} | {'accuracy': 0.8776} |
| 0.2771 | 31.0 | 992 | 0.8903 | {'f1': 0.8518992744344858} | {'accuracy': 0.8612} |
| 0.0305 | 32.0 | 1024 | 0.7611 | {'f1': 0.8783314020857473} | {'accuracy': 0.874} |
| 0.0305 | 33.0 | 1056 | 0.7911 | {'f1': 0.8707865168539325} | {'accuracy': 0.8712} |
| 0.0305 | 34.0 | 1088 | 0.8785 | {'f1': 0.8579807289484709} | {'accuracy': 0.8644} |
| 0.0305 | 35.0 | 1120 | 0.8127 | {'f1': 0.8705501618122977} | {'accuracy': 0.872} |
| 0.0305 | 36.0 | 1152 | 0.8361 | {'f1': 0.8663406682966585} | {'accuracy': 0.8688} |
| 0.0305 | 37.0 | 1184 | 0.8104 | {'f1': 0.8793565683646112} | {'accuracy': 0.874} |
| 0.0305 | 38.0 | 1216 | 0.8189 | {'f1': 0.875993640699523} | {'accuracy': 0.8752} |
| 0.0305 | 39.0 | 1248 | 0.8209 | {'f1': 0.8819390148553558} | {'accuracy': 0.8792} |
| 0.0305 | 40.0 | 1280 | 0.8555 | {'f1': 0.869951534733441} | {'accuracy': 0.8712} |
| 0.0305 | 41.0 | 1312 | 0.8538 | {'f1': 0.8732849071832123} | {'accuracy': 0.8744} |
| 0.0305 | 42.0 | 1344 | 0.8486 | {'f1': 0.8809433244579689} | {'accuracy': 0.8748} |
| 0.0305 | 43.0 | 1376 | 0.8763 | {'f1': 0.8746473196291817} | {'accuracy': 0.8756} |
| 0.0305 | 44.0 | 1408 | 0.9639 | {'f1': 0.855090832277144} | {'accuracy': 0.8628} |
| 0.0305 | 45.0 | 1440 | 0.8495 | {'f1': 0.8760064412238325} | {'accuracy': 0.8768} |
| 0.0305 | 46.0 | 1472 | 0.8497 | {'f1': 0.8831269349845201} | {'accuracy': 0.8792} |
| 0.0078 | 47.0 | 1504 | 0.8562 | {'f1': 0.8769470404984425} | {'accuracy': 0.8736} |
| 0.0078 | 48.0 | 1536 | 0.8552 | {'f1': 0.8782985427333596} | {'accuracy': 0.8764} |
| 0.0078 | 49.0 | 1568 | 0.8558 | {'f1': 0.8799682034976153} | {'accuracy': 0.8792} |
| 0.0078 | 50.0 | 1600 | 0.8696 | {'f1': 0.8746473196291817} | {'accuracy': 0.8756} |
| 0.0078 | 51.0 | 1632 | 0.9342 | {'f1': 0.87173100871731} | {'accuracy': 0.8764} |
| 0.0078 | 52.0 | 1664 | 0.9011 | {'f1': 0.8756137479541735} | {'accuracy': 0.8784} |
| 0.0078 | 53.0 | 1696 | 0.9044 | {'f1': 0.8776094965206713} | {'accuracy': 0.8804} |
| 0.0078 | 54.0 | 1728 | 0.8767 | {'f1': 0.8836465413834467} | {'accuracy': 0.8836} |
| 0.0078 | 55.0 | 1760 | 0.9982 | {'f1': 0.8648421052631577} | {'accuracy': 0.8716} |
| 0.0078 | 56.0 | 1792 | 0.8801 | {'f1': 0.8829915560916767} | {'accuracy': 0.8836} |
| 0.0078 | 57.0 | 1824 | 0.8925 | {'f1': 0.8877862595419846} | {'accuracy': 0.8824} |
| 0.0078 | 58.0 | 1856 | 1.0050 | {'f1': 0.8615902397980647} | {'accuracy': 0.8684} |
| 0.0078 | 59.0 | 1888 | 1.0207 | {'f1': 0.8595458368376786} | {'accuracy': 0.8664} |
| 0.0078 | 60.0 | 1920 | 0.9567 | {'f1': 0.8717320261437909} | {'accuracy': 0.8744} |
| 0.0078 | 61.0 | 1952 | 0.9195 | {'f1': 0.8792307692307691} | {'accuracy': 0.8744} |
| 0.0078 | 62.0 | 1984 | 0.9066 | {'f1': 0.875993640699523} | {'accuracy': 0.8752} |
| 0.0049 | 63.0 | 2016 | 1.0266 | {'f1': 0.8649100794646591} | {'accuracy': 0.8708} |
| 0.0049 | 64.0 | 2048 | 0.9384 | {'f1': 0.8845283018867924} | {'accuracy': 0.8776} |
| 0.0049 | 65.0 | 2080 | 0.9161 | {'f1': 0.8813291139240507} | {'accuracy': 0.88} |
| 0.0049 | 66.0 | 2112 | 0.9102 | {'f1': 0.8825147347740667} | {'accuracy': 0.8804} |
| 0.0049 | 67.0 | 2144 | 0.9218 | {'f1': 0.8806089743589743} | {'accuracy': 0.8808} |
| 0.0049 | 68.0 | 2176 | 1.1598 | {'f1': 0.8512644663523362} | {'accuracy': 0.8612} |
| 0.0049 | 69.0 | 2208 | 0.9406 | {'f1': 0.8753507014028056} | {'accuracy': 0.8756} |
| 0.0049 | 70.0 | 2240 | 0.9452 | {'f1': 0.8788230739450251} | {'accuracy': 0.8748} |
| 0.0049 | 71.0 | 2272 | 0.9634 | {'f1': 0.8741455568958586} | {'accuracy': 0.8748} |
| 0.0049 | 72.0 | 2304 | 1.0028 | {'f1': 0.8814703675918979} | {'accuracy': 0.8736} |
| 0.0049 | 73.0 | 2336 | 0.9469 | {'f1': 0.875801282051282} | {'accuracy': 0.876} |
| 0.0049 | 74.0 | 2368 | 1.0397 | {'f1': 0.8634655532359082} | {'accuracy': 0.8692} |
| 0.0049 | 75.0 | 2400 | 0.9316 | {'f1': 0.8852713178294573} | {'accuracy': 0.8816} |
| 0.0049 | 76.0 | 2432 | 0.9465 | {'f1': 0.8768433638899961} | {'accuracy': 0.8764} |
| 0.0049 | 77.0 | 2464 | 0.9301 | {'f1': 0.8873456790123456} | {'accuracy': 0.8832} |
| 0.0049 | 78.0 | 2496 | 1.0604 | {'f1': 0.8671386922115786} | {'accuracy': 0.8724} |
| 0.004 | 79.0 | 2528 | 0.9293 | {'f1': 0.8854247856586126} | {'accuracy': 0.8824} |
| 0.004 | 80.0 | 2560 | 0.9323 | {'f1': 0.88242233582383} | {'accuracy': 0.8804} |
| 0.004 | 81.0 | 2592 | 0.9347 | {'f1': 0.887253002712127} | {'accuracy': 0.8836} |
| 0.004 | 82.0 | 2624 | 0.9612 | {'f1': 0.8804828973843059} | {'accuracy': 0.8812} |
| 0.004 | 83.0 | 2656 | 0.9528 | {'f1': 0.8808664259927798} | {'accuracy': 0.8812} |
| 0.004 | 84.0 | 2688 | 0.9442 | {'f1': 0.8846459824980112} | {'accuracy': 0.884} |
| 0.004 | 85.0 | 2720 | 0.9392 | {'f1': 0.8828402366863906} | {'accuracy': 0.8812} |
| 0.004 | 86.0 | 2752 | 1.0638 | {'f1': 0.8701461377870564} | {'accuracy': 0.8756} |
| 0.004 | 87.0 | 2784 | 0.9640 | {'f1': 0.8866615265998459} | {'accuracy': 0.8824} |
| 0.004 | 88.0 | 2816 | 1.0389 | {'f1': 0.871900826446281} | {'accuracy': 0.876} |
| 0.004 | 89.0 | 2848 | 0.9569 | {'f1': 0.8879310344827586} | {'accuracy': 0.8856} |
| 0.004 | 90.0 | 2880 | 0.9986 | {'f1': 0.887121212121212} | {'accuracy': 0.8808} |
| 0.004 | 91.0 | 2912 | 1.0599 | {'f1': 0.8691666666666666} | {'accuracy': 0.8744} |
| 0.004 | 92.0 | 2944 | 0.9708 | {'f1': 0.8788368336025848} | {'accuracy': 0.88} |
| 0.004 | 93.0 | 2976 | 1.0033 | {'f1': 0.8741830065359477} | {'accuracy': 0.8768} |
| 0.0008 | 94.0 | 3008 | 1.2071 | {'f1': 0.8493975903614457} | {'accuracy': 0.86} |
| 0.0008 | 95.0 | 3040 | 1.0422 | {'f1': 0.8738664468260511} | {'accuracy': 0.8776} |
| 0.0008 | 96.0 | 3072 | 1.0542 | {'f1': 0.8808988764044944} | {'accuracy': 0.8728} |
| 0.0008 | 97.0 | 3104 | 1.0081 | {'f1': 0.8756624541377903} | {'accuracy': 0.878} |
| 0.0008 | 98.0 | 3136 | 0.9592 | {'f1': 0.8885387948011029} | {'accuracy': 0.8868} |
| 0.0008 | 99.0 | 3168 | 0.9641 | {'f1': 0.8862042088854248} | {'accuracy': 0.8832} |
| 0.0008 | 100.0 | 3200 | 0.9594 | {'f1': 0.8891518737672585} | {'accuracy': 0.8876} |
| 0.0008 | 101.0 | 3232 | 0.9607 | {'f1': 0.8886246531906462} | {'accuracy': 0.8876} |
| 0.0008 | 102.0 | 3264 | 1.2907 | {'f1': 0.8712230215827338} | {'accuracy': 0.8568} |
| 0.0008 | 103.0 | 3296 | 0.9945 | {'f1': 0.8863366336633663} | {'accuracy': 0.8852} |
| 0.0008 | 104.0 | 3328 | 1.0011 | {'f1': 0.8858049167327517} | {'accuracy': 0.8848} |
| 0.0008 | 105.0 | 3360 | 1.0414 | {'f1': 0.8841625522218002} | {'accuracy': 0.878} |
| 0.0008 | 106.0 | 3392 | 1.0385 | {'f1': 0.8717948717948717} | {'accuracy': 0.876} |
| 0.0008 | 107.0 | 3424 | 1.0569 | {'f1': 0.8660565723793678} | {'accuracy': 0.8712} |
| 0.0008 | 108.0 | 3456 | 1.0613 | {'f1': 0.8819133034379671} | {'accuracy': 0.8736} |
| 0.0008 | 109.0 | 3488 | 0.9667 | {'f1': 0.88315748339195} | {'accuracy': 0.8804} |
| 0.0048 | 110.0 | 3520 | 1.1289 | {'f1': 0.8781744571218255} | {'accuracy': 0.8676} |
| 0.0048 | 111.0 | 3552 | 0.9331 | {'f1': 0.8907563025210083} | {'accuracy': 0.8908} |
| 0.0048 | 112.0 | 3584 | 0.9808 | {'f1': 0.8881278538812785} | {'accuracy': 0.8824} |
| 0.0048 | 113.0 | 3616 | 0.9513 | {'f1': 0.8845843422114609} | {'accuracy': 0.8856} |
| 0.0048 | 114.0 | 3648 | 0.9608 | {'f1': 0.8874172185430463} | {'accuracy': 0.8844} |
| 0.0048 | 115.0 | 3680 | 0.9735 | {'f1': 0.8849005072181039} | {'accuracy': 0.882} |
| 0.0048 | 116.0 | 3712 | 0.9755 | {'f1': 0.8849627012171182} | {'accuracy': 0.8828} |
| 0.0048 | 117.0 | 3744 | 1.0475 | {'f1': 0.8888888888888888} | {'accuracy': 0.882} |
| 0.0048 | 118.0 | 3776 | 1.0445 | {'f1': 0.8785890073831009} | {'accuracy': 0.8816} |
| 0.0048 | 119.0 | 3808 | 0.9943 | {'f1': 0.8844621513944223} | {'accuracy': 0.884} |
| 0.0048 | 120.0 | 3840 | 1.0380 | {'f1': 0.8823529411764706} | {'accuracy': 0.8848} |
| 0.0048 | 121.0 | 3872 | 1.1418 | {'f1': 0.8846011131725418} | {'accuracy': 0.8756} |
| 0.0048 | 122.0 | 3904 | 1.0418 | {'f1': 0.8747954173486089} | {'accuracy': 0.8776} |
| 0.0048 | 123.0 | 3936 | 1.0097 | {'f1': 0.8817377312952533} | {'accuracy': 0.8824} |
| 0.0048 | 124.0 | 3968 | 0.9912 | {'f1': 0.8861852433281004} | {'accuracy': 0.884} |
| 0.0029 | 125.0 | 4000 | 0.9924 | {'f1': 0.8879310344827586} | {'accuracy': 0.8856} |
| 0.0029 | 126.0 | 4032 | 0.9964 | {'f1': 0.8843861740166864} | {'accuracy': 0.8836} |
| 0.0029 | 127.0 | 4064 | 0.9966 | {'f1': 0.8844779674473997} | {'accuracy': 0.8836} |
| 0.0029 | 128.0 | 4096 | 1.0560 | {'f1': 0.8745901639344262} | {'accuracy': 0.8776} |
| 0.0029 | 129.0 | 4128 | 1.0364 | {'f1': 0.8800648298217179} | {'accuracy': 0.8816} |
| 0.0029 | 130.0 | 4160 | 1.0233 | {'f1': 0.8804828973843059} | {'accuracy': 0.8812} |
| 0.0029 | 131.0 | 4192 | 1.0493 | {'f1': 0.889904761904762} | {'accuracy': 0.8844} |
| 0.0029 | 132.0 | 4224 | 1.0439 | {'f1': 0.8893991580558744} | {'accuracy': 0.8844} |
| 0.0029 | 133.0 | 4256 | 1.0264 | {'f1': 0.8906068805566293} | {'accuracy': 0.8868} |
| 0.0029 | 134.0 | 4288 | 1.1016 | {'f1': 0.8866442199775532} | {'accuracy': 0.8788} |
| 0.0029 | 135.0 | 4320 | 1.0469 | {'f1': 0.8895658796648896} | {'accuracy': 0.884} |
| 0.0029 | 136.0 | 4352 | 1.1812 | {'f1': 0.8828297715549005} | {'accuracy': 0.8728} |
| 0.0029 | 137.0 | 4384 | 1.0357 | {'f1': 0.8940905602455871} | {'accuracy': 0.8896} |
| 0.0029 | 138.0 | 4416 | 1.1247 | {'f1': 0.8776266996291718} | {'accuracy': 0.8812} |
| 0.0029 | 139.0 | 4448 | 1.0886 | {'f1': 0.8932478310071671} | {'accuracy': 0.8868} |
| 0.0029 | 140.0 | 4480 | 1.0707 | {'f1': 0.8932626797880393} | {'accuracy': 0.8872} |
| 0.0022 | 141.0 | 4512 | 1.0439 | {'f1': 0.8868378812199037} | {'accuracy': 0.8872} |
| 0.0022 | 142.0 | 4544 | 1.0858 | {'f1': 0.8846310640032613} | {'accuracy': 0.8868} |
| 0.0022 | 143.0 | 4576 | 1.0295 | {'f1': 0.8903071400079777} | {'accuracy': 0.89} |
| 0.0022 | 144.0 | 4608 | 1.0280 | {'f1': 0.8903945795137506} | {'accuracy': 0.89} |
| 0.0022 | 145.0 | 4640 | 1.0323 | {'f1': 0.8898643256185156} | {'accuracy': 0.8896} |
| 0.0022 | 146.0 | 4672 | 1.0339 | {'f1': 0.8898643256185156} | {'accuracy': 0.8896} |
| 0.0022 | 147.0 | 4704 | 1.0161 | {'f1': 0.8920409771473602} | {'accuracy': 0.8904} |
| 0.0022 | 148.0 | 4736 | 1.2549 | {'f1': 0.861850443599493} | {'accuracy': 0.8692} |
| 0.0022 | 149.0 | 4768 | 1.0567 | {'f1': 0.8887996788438377} | {'accuracy': 0.8892} |
| 0.0022 | 150.0 | 4800 | 1.0522 | {'f1': 0.8907563025210083} | {'accuracy': 0.8908} |
| 0.0022 | 151.0 | 4832 | 1.0526 | {'f1': 0.8907563025210083} | {'accuracy': 0.8908} |
| 0.0022 | 152.0 | 4864 | 1.2013 | {'f1': 0.8857774502579219} | {'accuracy': 0.876} |
| 0.0022 | 153.0 | 4896 | 1.1488 | {'f1': 0.8760806916426512} | {'accuracy': 0.8796} |
| 0.0022 | 154.0 | 4928 | 1.0654 | {'f1': 0.8832731648616126} | {'accuracy': 0.8836} |
| 0.0022 | 155.0 | 4960 | 1.0409 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0022 | 156.0 | 4992 | 1.3100 | {'f1': 0.8513918629550321} | {'accuracy': 0.8612} |
| 0.001 | 157.0 | 5024 | 1.2608 | {'f1': 0.8628113127902068} | {'accuracy': 0.87} |
| 0.001 | 158.0 | 5056 | 1.0549 | {'f1': 0.8910355486862442} | {'accuracy': 0.8872} |
| 0.001 | 159.0 | 5088 | 1.1585 | {'f1': 0.8762844225236333} | {'accuracy': 0.8796} |
| 0.001 | 160.0 | 5120 | 1.1419 | {'f1': 0.879542670477746} | {'accuracy': 0.882} |
| 0.001 | 161.0 | 5152 | 1.1148 | {'f1': 0.8810963321241435} | {'accuracy': 0.882} |
| 0.001 | 162.0 | 5184 | 1.1114 | {'f1': 0.8807413376309429} | {'accuracy': 0.8816} |
| 0.001 | 163.0 | 5216 | 1.1111 | {'f1': 0.8811921063229964} | {'accuracy': 0.882} |
| 0.001 | 164.0 | 5248 | 1.1205 | {'f1': 0.8838141025641026} | {'accuracy': 0.884} |
| 0.001 | 165.0 | 5280 | 1.1270 | {'f1': 0.8842611133360032} | {'accuracy': 0.8844} |
| 0.001 | 166.0 | 5312 | 1.2663 | {'f1': 0.8605042016806723} | {'accuracy': 0.8672} |
| 0.001 | 167.0 | 5344 | 1.0968 | {'f1': 0.8861267040898154} | {'accuracy': 0.8864} |
| 0.001 | 168.0 | 5376 | 1.3010 | {'f1': 0.8606522659889877} | {'accuracy': 0.8684} |
| 0.001 | 169.0 | 5408 | 1.1075 | {'f1': 0.880161943319838} | {'accuracy': 0.8816} |
| 0.001 | 170.0 | 5440 | 1.1110 | {'f1': 0.8819472616632859} | {'accuracy': 0.8836} |
| 0.001 | 171.0 | 5472 | 1.0844 | {'f1': 0.8806387225548902} | {'accuracy': 0.8804} |
| 0.0024 | 172.0 | 5504 | 1.4479 | {'f1': 0.8404163052905465} | {'accuracy': 0.8528} |
| 0.0024 | 173.0 | 5536 | 1.1518 | {'f1': 0.8859516616314198} | {'accuracy': 0.8792} |
| 0.0024 | 174.0 | 5568 | 1.2326 | {'f1': 0.8644351464435147} | {'accuracy': 0.8704} |
| 0.0024 | 175.0 | 5600 | 1.1863 | {'f1': 0.8912228057014252} | {'accuracy': 0.884} |
| 0.0024 | 176.0 | 5632 | 1.1230 | {'f1': 0.8864908073541168} | {'accuracy': 0.8864} |
| 0.0024 | 177.0 | 5664 | 1.2142 | {'f1': 0.8680497925311204} | {'accuracy': 0.8728} |
| 0.0024 | 178.0 | 5696 | 1.3023 | {'f1': 0.8589527027027027} | {'accuracy': 0.8664} |
| 0.0024 | 179.0 | 5728 | 1.1757 | {'f1': 0.8898081985708913} | {'accuracy': 0.8828} |
| 0.0024 | 180.0 | 5760 | 1.2237 | {'f1': 0.8703933747412008} | {'accuracy': 0.8748} |
| 0.0024 | 181.0 | 5792 | 1.1846 | {'f1': 0.8744872846595569} | {'accuracy': 0.8776} |
| 0.0024 | 182.0 | 5824 | 1.1774 | {'f1': 0.8748977923139819} | {'accuracy': 0.8776} |
| 0.0024 | 183.0 | 5856 | 1.1206 | {'f1': 0.8826591910292352} | {'accuracy': 0.8828} |
| 0.0024 | 184.0 | 5888 | 1.1166 | {'f1': 0.8827531012404962} | {'accuracy': 0.8828} |
| 0.0024 | 185.0 | 5920 | 1.1179 | {'f1': 0.8827531012404962} | {'accuracy': 0.8828} |
| 0.0024 | 186.0 | 5952 | 1.1217 | {'f1': 0.8826591910292352} | {'accuracy': 0.8828} |
| 0.0024 | 187.0 | 5984 | 1.1211 | {'f1': 0.8823058446757407} | {'accuracy': 0.8824} |
| 0.0019 | 188.0 | 6016 | 1.1497 | {'f1': 0.8939566704675029} | {'accuracy': 0.8884} |
| 0.0019 | 189.0 | 6048 | 1.0649 | {'f1': 0.8934681181959565} | {'accuracy': 0.8904} |
| 0.0019 | 190.0 | 6080 | 1.1508 | {'f1': 0.8797364085667216} | {'accuracy': 0.8832} |
| 0.0019 | 191.0 | 6112 | 1.0691 | {'f1': 0.885193982581156} | {'accuracy': 0.884} |
| 0.0019 | 192.0 | 6144 | 1.0697 | {'f1': 0.8856351404827859} | {'accuracy': 0.8844} |
| 0.0019 | 193.0 | 6176 | 1.0720 | {'f1': 0.8846611177170035} | {'accuracy': 0.8836} |
| 0.0019 | 194.0 | 6208 | 1.0872 | {'f1': 0.8832} | {'accuracy': 0.8832} |
| 0.0019 | 195.0 | 6240 | 1.1084 | {'f1': 0.8819725141471302} | {'accuracy': 0.8832} |
| 0.0019 | 196.0 | 6272 | 1.1100 | {'f1': 0.8819725141471302} | {'accuracy': 0.8832} |
| 0.0019 | 197.0 | 6304 | 1.1093 | {'f1': 0.8816161616161616} | {'accuracy': 0.8828} |
| 0.0019 | 198.0 | 6336 | 1.1084 | {'f1': 0.8829701372074253} | {'accuracy': 0.884} |
| 0.0019 | 199.0 | 6368 | 1.1088 | {'f1': 0.8829701372074253} | {'accuracy': 0.884} |
| 0.0019 | 200.0 | 6400 | 1.1076 | {'f1': 0.8830645161290323} | {'accuracy': 0.884} |
| 0.0019 | 201.0 | 6432 | 1.1078 | {'f1': 0.8830645161290323} | {'accuracy': 0.884} |
| 0.0019 | 202.0 | 6464 | 1.3658 | {'f1': 0.8515021459227468} | {'accuracy': 0.8616} |
| 0.0019 | 203.0 | 6496 | 1.7765 | {'f1': 0.8077969174977335} | {'accuracy': 0.8304} |
| 0.0042 | 204.0 | 6528 | 1.3374 | {'f1': 0.8572638712409996} | {'accuracy': 0.8652} |
| 0.0042 | 205.0 | 6560 | 1.3661 | {'f1': 0.8531049250535331} | {'accuracy': 0.8628} |
| 0.0042 | 206.0 | 6592 | 1.0987 | {'f1': 0.8928707586732749} | {'accuracy': 0.8876} |
| 0.0042 | 207.0 | 6624 | 1.0845 | {'f1': 0.8939103791650709} | {'accuracy': 0.8892} |
| 0.0042 | 208.0 | 6656 | 1.0750 | {'f1': 0.893420546363986} | {'accuracy': 0.8892} |
| 0.0042 | 209.0 | 6688 | 1.0673 | {'f1': 0.8939628482972137} | {'accuracy': 0.8904} |
| 0.0042 | 210.0 | 6720 | 1.0674 | {'f1': 0.8941450174486235} | {'accuracy': 0.8908} |
| 0.0042 | 211.0 | 6752 | 1.0677 | {'f1': 0.8943278943278943} | {'accuracy': 0.8912} |
| 0.0042 | 212.0 | 6784 | 1.0793 | {'f1': 0.8924148606811146} | {'accuracy': 0.8888} |
| 0.0042 | 213.0 | 6816 | 1.0959 | {'f1': 0.8902532617037606} | {'accuracy': 0.8856} |
| 0.0042 | 214.0 | 6848 | 1.2329 | {'f1': 0.8674399337199669} | {'accuracy': 0.872} |
| 0.0042 | 215.0 | 6880 | 1.1383 | {'f1': 0.8819024586860137} | {'accuracy': 0.8828} |
| 0.0042 | 216.0 | 6912 | 1.1344 | {'f1': 0.8846926476496585} | {'accuracy': 0.8852} |
| 0.0042 | 217.0 | 6944 | 1.1316 | {'f1': 0.8860353130016051} | {'accuracy': 0.8864} |
| 0.0042 | 218.0 | 6976 | 1.1284 | {'f1': 0.8866639967961554} | {'accuracy': 0.8868} |
| 0.0009 | 219.0 | 7008 | 1.1253 | {'f1': 0.8864908073541168} | {'accuracy': 0.8864} |
| 0.0009 | 220.0 | 7040 | 1.1245 | {'f1': 0.8857827476038339} | {'accuracy': 0.8856} |
| 0.0009 | 221.0 | 7072 | 1.1242 | {'f1': 0.8857827476038339} | {'accuracy': 0.8856} |
| 0.0009 | 222.0 | 7104 | 1.1136 | {'f1': 0.8857142857142859} | {'accuracy': 0.8848} |
| 0.0009 | 223.0 | 7136 | 1.1104 | {'f1': 0.8873128447596532} | {'accuracy': 0.8856} |
| 0.0009 | 224.0 | 7168 | 1.1184 | {'f1': 0.8867699642431466} | {'accuracy': 0.886} |
| 0.0009 | 225.0 | 7200 | 1.1197 | {'f1': 0.8858846918489065} | {'accuracy': 0.8852} |
| 0.0009 | 226.0 | 7232 | 1.1202 | {'f1': 0.8858846918489065} | {'accuracy': 0.8852} |
| 0.0009 | 227.0 | 7264 | 1.1212 | {'f1': 0.8854415274463007} | {'accuracy': 0.8848} |
| 0.0009 | 228.0 | 7296 | 1.1214 | {'f1': 0.8858846918489065} | {'accuracy': 0.8852} |
| 0.0009 | 229.0 | 7328 | 1.1217 | {'f1': 0.8858846918489065} | {'accuracy': 0.8852} |
| 0.0009 | 230.0 | 7360 | 1.2362 | {'f1': 0.8856502242152466} | {'accuracy': 0.8776} |
| 0.0009 | 231.0 | 7392 | 1.2124 | {'f1': 0.8763769889840881} | {'accuracy': 0.8788} |
| 0.0009 | 232.0 | 7424 | 1.1419 | {'f1': 0.8844779674473997} | {'accuracy': 0.8836} |
| 0.0009 | 233.0 | 7456 | 1.1410 | {'f1': 0.8842188739095956} | {'accuracy': 0.8832} |
| 0.0009 | 234.0 | 7488 | 1.1424 | {'f1': 0.8849206349206349} | {'accuracy': 0.884} |
| 0.0004 | 235.0 | 7520 | 1.1459 | {'f1': 0.8830548926014321} | {'accuracy': 0.8824} |
| 0.0004 | 236.0 | 7552 | 1.1737 | {'f1': 0.8801287208366854} | {'accuracy': 0.8808} |
| 0.0004 | 237.0 | 7584 | 1.1743 | {'f1': 0.8804828973843059} | {'accuracy': 0.8812} |
| 0.0004 | 238.0 | 7616 | 1.1412 | {'f1': 0.8854660347551343} | {'accuracy': 0.884} |
| 0.0004 | 239.0 | 7648 | 1.1411 | {'f1': 0.8854660347551343} | {'accuracy': 0.884} |
| 0.0004 | 240.0 | 7680 | 1.3032 | {'f1': 0.867109634551495} | {'accuracy': 0.872} |
| 0.0004 | 241.0 | 7712 | 1.3155 | {'f1': 0.86511240632806} | {'accuracy': 0.8704} |
| 0.0004 | 242.0 | 7744 | 1.3813 | {'f1': 0.8608659100462379} | {'accuracy': 0.8676} |
| 0.0004 | 243.0 | 7776 | 1.5158 | {'f1': 0.8496110630942092} | {'accuracy': 0.8608} |
| 0.0004 | 244.0 | 7808 | 1.3354 | {'f1': 0.875724937862469} | {'accuracy': 0.88} |
| 0.0004 | 245.0 | 7840 | 1.2804 | {'f1': 0.8803905614320586} | {'accuracy': 0.8824} |
| 0.0004 | 246.0 | 7872 | 1.2878 | {'f1': 0.8794442174090723} | {'accuracy': 0.882} |
| 0.0004 | 247.0 | 7904 | 1.3296 | {'f1': 0.8722612649855312} | {'accuracy': 0.8764} |
| 0.0004 | 248.0 | 7936 | 1.2071 | {'f1': 0.8958093041138023} | {'accuracy': 0.8916} |
| 0.0004 | 249.0 | 7968 | 1.2093 | {'f1': 0.8963133640552995} | {'accuracy': 0.892} |
| 0.0022 | 250.0 | 8000 | 1.1794 | {'f1': 0.8939628482972137} | {'accuracy': 0.8904} |
| 0.0022 | 251.0 | 8032 | 1.1944 | {'f1': 0.895648825567963} | {'accuracy': 0.8916} |
| 0.0022 | 252.0 | 8064 | 1.1748 | {'f1': 0.8941450174486235} | {'accuracy': 0.8908} |
| 0.0022 | 253.0 | 8096 | 1.1720 | {'f1': 0.8939805825242718} | {'accuracy': 0.8908} |
| 0.0022 | 254.0 | 8128 | 1.2334 | {'f1': 0.8792822185970636} | {'accuracy': 0.8816} |
| 0.0022 | 255.0 | 8160 | 1.1558 | {'f1': 0.8971672487388436} | {'accuracy': 0.894} |
| 0.0022 | 256.0 | 8192 | 1.1672 | {'f1': 0.8983050847457628} | {'accuracy': 0.8944} |
| 0.0022 | 257.0 | 8224 | 1.1623 | {'f1': 0.8991109393119444} | {'accuracy': 0.8956} |
| 0.0022 | 258.0 | 8256 | 1.1615 | {'f1': 0.8990328820116054} | {'accuracy': 0.8956} |
| 0.0022 | 259.0 | 8288 | 1.1585 | {'f1': 0.8984496124031008} | {'accuracy': 0.8952} |
| 0.0022 | 260.0 | 8320 | 1.1550 | {'f1': 0.8968470221876217} | {'accuracy': 0.894} |
| 0.0022 | 261.0 | 8352 | 1.1552 | {'f1': 0.8968470221876217} | {'accuracy': 0.894} |
| 0.0022 | 262.0 | 8384 | 1.1553 | {'f1': 0.8964174454828661} | {'accuracy': 0.8936} |
| 0.0022 | 263.0 | 8416 | 1.1555 | {'f1': 0.8964174454828661} | {'accuracy': 0.8936} |
| 0.0022 | 264.0 | 8448 | 1.2035 | {'f1': 0.8860145513338723} | {'accuracy': 0.8872} |
| 0.0022 | 265.0 | 8480 | 1.2186 | {'f1': 0.8840227088402272} | {'accuracy': 0.8856} |
| 0.0007 | 266.0 | 8512 | 1.2153 | {'f1': 0.8845686512758202} | {'accuracy': 0.886} |
| 0.0007 | 267.0 | 8544 | 1.2144 | {'f1': 0.8850202429149797} | {'accuracy': 0.8864} |
| 0.0007 | 268.0 | 8576 | 1.2137 | {'f1': 0.8850202429149797} | {'accuracy': 0.8864} |
| 0.0007 | 269.0 | 8608 | 1.2133 | {'f1': 0.8850202429149797} | {'accuracy': 0.8864} |
| 0.0007 | 270.0 | 8640 | 1.2131 | {'f1': 0.8850202429149797} | {'accuracy': 0.8864} |
| 0.0007 | 271.0 | 8672 | 1.2128 | {'f1': 0.8854714690408741} | {'accuracy': 0.8868} |
| 0.0007 | 272.0 | 8704 | 1.2123 | {'f1': 0.8855640921957139} | {'accuracy': 0.8868} |
| 0.0007 | 273.0 | 8736 | 1.2120 | {'f1': 0.8855640921957139} | {'accuracy': 0.8868} |
| 0.0007 | 274.0 | 8768 | 1.2055 | {'f1': 0.8850342880193627} | {'accuracy': 0.886} |
| 0.0007 | 275.0 | 8800 | 1.2049 | {'f1': 0.885483870967742} | {'accuracy': 0.8864} |
| 0.0007 | 276.0 | 8832 | 1.1718 | {'f1': 0.888178913738019} | {'accuracy': 0.888} |
| 0.0007 | 277.0 | 8864 | 1.1650 | {'f1': 0.8901273885350319} | {'accuracy': 0.8896} |
| 0.0007 | 278.0 | 8896 | 1.1606 | {'f1': 0.89179548156956} | {'accuracy': 0.8908} |
| 0.0007 | 279.0 | 8928 | 1.1608 | {'f1': 0.8914421553090333} | {'accuracy': 0.8904} |
| 0.0007 | 280.0 | 8960 | 1.1609 | {'f1': 0.8914421553090333} | {'accuracy': 0.8904} |
| 0.0007 | 281.0 | 8992 | 1.1613 | {'f1': 0.8914421553090333} | {'accuracy': 0.8904} |
| 0.0 | 282.0 | 9024 | 1.1623 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 283.0 | 9056 | 1.1636 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 284.0 | 9088 | 1.1638 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 285.0 | 9120 | 1.1642 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 286.0 | 9152 | 1.1645 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 287.0 | 9184 | 1.1647 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 288.0 | 9216 | 1.1649 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 289.0 | 9248 | 1.1652 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 290.0 | 9280 | 1.1657 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 291.0 | 9312 | 1.1659 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 292.0 | 9344 | 1.1661 | {'f1': 0.8913560666137985} | {'accuracy': 0.8904} |
| 0.0 | 293.0 | 9376 | 1.1812 | {'f1': 0.888178913738019} | {'accuracy': 0.888} |
| 0.0 | 294.0 | 9408 | 1.1723 | {'f1': 0.8900357284636761} | {'accuracy': 0.8892} |
| 0.0 | 295.0 | 9440 | 1.1702 | {'f1': 0.8912613681296955} | {'accuracy': 0.89} |
| 0.0 | 296.0 | 9472 | 1.1705 | {'f1': 0.8912613681296955} | {'accuracy': 0.89} |
| 0.0 | 297.0 | 9504 | 1.1708 | {'f1': 0.891699604743083} | {'accuracy': 0.8904} |
| 0.0 | 298.0 | 9536 | 1.1712 | {'f1': 0.891699604743083} | {'accuracy': 0.8904} |
| 0.0 | 299.0 | 9568 | 1.1715 | {'f1': 0.891699604743083} | {'accuracy': 0.8904} |
| 0.0 | 300.0 | 9600 | 1.1717 | {'f1': 0.891699604743083} | {'accuracy': 0.8904} |
| 0.0 | 301.0 | 9632 | 1.1851 | {'f1': 0.8887116074990027} | {'accuracy': 0.8884} |
| 0.0 | 302.0 | 9664 | 1.1968 | {'f1': 0.8879103282626101} | {'accuracy': 0.888} |
| 0.0 | 303.0 | 9696 | 1.1972 | {'f1': 0.8879103282626101} | {'accuracy': 0.888} |
| 0.0 | 304.0 | 9728 | 1.1972 | {'f1': 0.8879103282626101} | {'accuracy': 0.888} |
| 0.0 | 305.0 | 9760 | 1.1968 | {'f1': 0.8883553421368547} | {'accuracy': 0.8884} |
| 0.0 | 306.0 | 9792 | 1.1966 | {'f1': 0.8883553421368547} | {'accuracy': 0.8884} |
| 0.0 | 307.0 | 9824 | 1.1965 | {'f1': 0.8883553421368547} | {'accuracy': 0.8884} |
| 0.0 | 308.0 | 9856 | 1.1963 | {'f1': 0.888} | {'accuracy': 0.888} |
| 0.0 | 309.0 | 9888 | 1.1968 | {'f1': 0.888} | {'accuracy': 0.888} |
| 0.0 | 310.0 | 9920 | 1.1967 | {'f1': 0.888} | {'accuracy': 0.888} |
| 0.0 | 311.0 | 9952 | 1.1967 | {'f1': 0.888} | {'accuracy': 0.888} |
| 0.0 | 312.0 | 9984 | 1.1955 | {'f1': 0.888178913738019} | {'accuracy': 0.888} |
| 0.0 | 313.0 | 10016 | 1.1928 | {'f1': 0.8886227544910179} | {'accuracy': 0.8884} |
| 0.0 | 314.0 | 10048 | 1.1926 | {'f1': 0.8886227544910179} | {'accuracy': 0.8884} |
| 0.0 | 315.0 | 10080 | 1.1930 | {'f1': 0.8886227544910179} | {'accuracy': 0.8884} |
| 0.0 | 316.0 | 10112 | 1.1934 | {'f1': 0.8886227544910179} | {'accuracy': 0.8884} |
| 0.0 | 317.0 | 10144 | 1.1932 | {'f1': 0.8887116074990027} | {'accuracy': 0.8884} |
| 0.0 | 318.0 | 10176 | 1.1932 | {'f1': 0.8891547049441787} | {'accuracy': 0.8888} |
| 0.0 | 319.0 | 10208 | 1.1933 | {'f1': 0.8891547049441787} | {'accuracy': 0.8888} |
| 0.0 | 320.0 | 10240 | 1.1934 | {'f1': 0.8891547049441787} | {'accuracy': 0.8888} |
| 0.0 | 321.0 | 10272 | 1.1935 | {'f1': 0.8888003188521324} | {'accuracy': 0.8884} |
| 0.0 | 322.0 | 10304 | 1.1936 | {'f1': 0.8888003188521324} | {'accuracy': 0.8884} |
| 0.0 | 323.0 | 10336 | 1.1857 | {'f1': 0.8922226608764311} | {'accuracy': 0.8908} |
| 0.0 | 324.0 | 10368 | 1.1834 | {'f1': 0.8939334637964774} | {'accuracy': 0.8916} |
| 0.0 | 325.0 | 10400 | 1.1840 | {'f1': 0.8940164254986311} | {'accuracy': 0.8916} |
| 0.0 | 326.0 | 10432 | 1.3728 | {'f1': 0.8917910447761194} | {'accuracy': 0.884} |
| 0.0 | 327.0 | 10464 | 1.3580 | {'f1': 0.8761354252683732} | {'accuracy': 0.88} |
| 0.0 | 328.0 | 10496 | 1.3400 | {'f1': 0.8796714579055441} | {'accuracy': 0.8828} |
| 0.0011 | 329.0 | 10528 | 1.2114 | {'f1': 0.8901229670765569} | {'accuracy': 0.8892} |
| 0.0011 | 330.0 | 10560 | 1.1982 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 331.0 | 10592 | 1.1982 | {'f1': 0.894385551629368} | {'accuracy': 0.8924} |
| 0.0011 | 332.0 | 10624 | 1.1985 | {'f1': 0.894385551629368} | {'accuracy': 0.8924} |
| 0.0011 | 333.0 | 10656 | 1.1988 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 334.0 | 10688 | 1.1991 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 335.0 | 10720 | 1.1994 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 336.0 | 10752 | 1.1996 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 337.0 | 10784 | 1.1998 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 338.0 | 10816 | 1.2000 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 339.0 | 10848 | 1.2004 | {'f1': 0.8939512961508248} | {'accuracy': 0.892} |
| 0.0011 | 340.0 | 10880 | 1.2012 | {'f1': 0.893432953204876} | {'accuracy': 0.8916} |
| 0.0011 | 341.0 | 10912 | 1.2018 | {'f1': 0.8925619834710744} | {'accuracy': 0.8908} |
| 0.0011 | 342.0 | 10944 | 1.2020 | {'f1': 0.8925619834710744} | {'accuracy': 0.8908} |
| 0.0011 | 343.0 | 10976 | 1.2029 | {'f1': 0.8924773532886963} | {'accuracy': 0.8908} |
| 0.0 | 344.0 | 11008 | 1.2033 | {'f1': 0.8920409771473602} | {'accuracy': 0.8904} |
| 0.0 | 345.0 | 11040 | 1.2037 | {'f1': 0.892392589672842} | {'accuracy': 0.8908} |
| 0.0 | 346.0 | 11072 | 1.2039 | {'f1': 0.892392589672842} | {'accuracy': 0.8908} |
| 0.0 | 347.0 | 11104 | 1.2042 | {'f1': 0.892392589672842} | {'accuracy': 0.8908} |
| 0.0 | 348.0 | 11136 | 1.2049 | {'f1': 0.8919558359621451} | {'accuracy': 0.8904} |
| 0.0 | 349.0 | 11168 | 1.2057 | {'f1': 0.8919558359621451} | {'accuracy': 0.8904} |
| 0.0 | 350.0 | 11200 | 1.2060 | {'f1': 0.8919558359621451} | {'accuracy': 0.8904} |
| 0.0 | 351.0 | 11232 | 1.2066 | {'f1': 0.8919558359621451} | {'accuracy': 0.8904} |
| 0.0 | 352.0 | 11264 | 1.2069 | {'f1': 0.8919558359621451} | {'accuracy': 0.8904} |
| 0.0 | 353.0 | 11296 | 1.2071 | {'f1': 0.8919558359621451} | {'accuracy': 0.8904} |
| 0.0 | 354.0 | 11328 | 1.3487 | {'f1': 0.8800328677074775} | {'accuracy': 0.8832} |
| 0.0 | 355.0 | 11360 | 1.3550 | {'f1': 0.8800328677074775} | {'accuracy': 0.8832} |
| 0.0 | 356.0 | 11392 | 1.3537 | {'f1': 0.8800328677074775} | {'accuracy': 0.8832} |
| 0.0 | 357.0 | 11424 | 1.3524 | {'f1': 0.8800328677074775} | {'accuracy': 0.8832} |
| 0.0 | 358.0 | 11456 | 1.3508 | {'f1': 0.8800328677074775} | {'accuracy': 0.8832} |
| 0.0 | 359.0 | 11488 | 1.3490 | {'f1': 0.8800328677074775} | {'accuracy': 0.8832} |
| 0.0 | 360.0 | 11520 | 1.3446 | {'f1': 0.8805908904390645} | {'accuracy': 0.8836} |
| 0.0 | 361.0 | 11552 | 1.2418 | {'f1': 0.8881814564265818} | {'accuracy': 0.8876} |
| 0.0 | 362.0 | 11584 | 1.2392 | {'f1': 0.8891537544696066} | {'accuracy': 0.8884} |
| 0.0 | 363.0 | 11616 | 1.2392 | {'f1': 0.8891537544696066} | {'accuracy': 0.8884} |
| 0.0 | 364.0 | 11648 | 1.6402 | {'f1': 0.8736955739474631} | {'accuracy': 0.8596} |
| 0.0 | 365.0 | 11680 | 1.3830 | {'f1': 0.8876488095238095} | {'accuracy': 0.8792} |
| 0.0 | 366.0 | 11712 | 1.4854 | {'f1': 0.8601694915254238} | {'accuracy': 0.868} |
| 0.0 | 367.0 | 11744 | 1.3072 | {'f1': 0.8802961744138215} | {'accuracy': 0.8836} |
| 0.0 | 368.0 | 11776 | 1.2977 | {'f1': 0.8811475409836066} | {'accuracy': 0.884} |
| 0.0 | 369.0 | 11808 | 1.2923 | {'f1': 0.8805237315875615} | {'accuracy': 0.8832} |
| 0.0 | 370.0 | 11840 | 1.4240 | {'f1': 0.8644997889404812} | {'accuracy': 0.8716} |
| 0.0 | 371.0 | 11872 | 1.1734 | {'f1': 0.8884462151394423} | {'accuracy': 0.888} |
| 0.0 | 372.0 | 11904 | 1.1621 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0 | 373.0 | 11936 | 1.1620 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0 | 374.0 | 11968 | 1.1630 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0044 | 375.0 | 12000 | 1.1642 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0044 | 376.0 | 12032 | 1.1644 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0044 | 377.0 | 12064 | 1.1646 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0044 | 378.0 | 12096 | 1.1645 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0044 | 379.0 | 12128 | 1.1651 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0044 | 380.0 | 12160 | 1.1641 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 381.0 | 12192 | 1.1641 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 382.0 | 12224 | 1.1643 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 383.0 | 12256 | 1.1638 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 384.0 | 12288 | 1.1636 | {'f1': 0.888713496448303} | {'accuracy': 0.8872} |
| 0.0044 | 385.0 | 12320 | 1.1643 | {'f1': 0.8886255924170617} | {'accuracy': 0.8872} |
| 0.0044 | 386.0 | 12352 | 1.1657 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 387.0 | 12384 | 1.1661 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 388.0 | 12416 | 1.1664 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 389.0 | 12448 | 1.1666 | {'f1': 0.8889766890557091} | {'accuracy': 0.8876} |
| 0.0044 | 390.0 | 12480 | 1.1680 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0 | 391.0 | 12512 | 1.1694 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0 | 392.0 | 12544 | 1.1705 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0 | 393.0 | 12576 | 1.1708 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0 | 394.0 | 12608 | 1.1710 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0 | 395.0 | 12640 | 1.1718 | {'f1': 0.889944576405384} | {'accuracy': 0.8888} |
| 0.0 | 396.0 | 12672 | 1.1720 | {'f1': 0.889944576405384} | {'accuracy': 0.8888} |
| 0.0 | 397.0 | 12704 | 1.1724 | {'f1': 0.889944576405384} | {'accuracy': 0.8888} |
| 0.0 | 398.0 | 12736 | 1.1727 | {'f1': 0.889944576405384} | {'accuracy': 0.8888} |
| 0.0 | 399.0 | 12768 | 1.1728 | {'f1': 0.889944576405384} | {'accuracy': 0.8888} |
| 0.0 | 400.0 | 12800 | 1.1731 | {'f1': 0.889592402057776} | {'accuracy': 0.8884} |
| 0.0 | 401.0 | 12832 | 1.1733 | {'f1': 0.889592402057776} | {'accuracy': 0.8884} |
| 0.0 | 402.0 | 12864 | 1.1735 | {'f1': 0.8892405063291139} | {'accuracy': 0.888} |
| 0.0 | 403.0 | 12896 | 1.1731 | {'f1': 0.888888888888889} | {'accuracy': 0.8876} |
| 0.0 | 404.0 | 12928 | 1.1707 | {'f1': 0.888713496448303} | {'accuracy': 0.8872} |
| 0.0 | 405.0 | 12960 | 1.1709 | {'f1': 0.8891518737672585} | {'accuracy': 0.8876} |
| 0.0 | 406.0 | 12992 | 1.3069 | {'f1': 0.880922950144211} | {'accuracy': 0.8844} |
| 0.0009 | 407.0 | 13024 | 1.1802 | {'f1': 0.8900398406374502} | {'accuracy': 0.8896} |
| 0.0009 | 408.0 | 13056 | 1.1781 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 409.0 | 13088 | 1.1782 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 410.0 | 13120 | 1.1784 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 411.0 | 13152 | 1.1790 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 412.0 | 13184 | 1.1791 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 413.0 | 13216 | 1.1792 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 414.0 | 13248 | 1.1793 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 415.0 | 13280 | 1.1795 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 416.0 | 13312 | 1.1796 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 417.0 | 13344 | 1.1800 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 418.0 | 13376 | 1.1803 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 419.0 | 13408 | 1.1809 | {'f1': 0.8914512922465209} | {'accuracy': 0.8908} |
| 0.0009 | 420.0 | 13440 | 1.1823 | {'f1': 0.8910103420843277} | {'accuracy': 0.8904} |
| 0.0009 | 421.0 | 13472 | 1.1827 | {'f1': 0.8910103420843277} | {'accuracy': 0.8904} |
| 0.0 | 422.0 | 13504 | 1.1829 | {'f1': 0.8910103420843277} | {'accuracy': 0.8904} |
| 0.0 | 423.0 | 13536 | 1.1830 | {'f1': 0.8910103420843277} | {'accuracy': 0.8904} |
| 0.0 | 424.0 | 13568 | 1.1831 | {'f1': 0.8910103420843277} | {'accuracy': 0.8904} |
| 0.0 | 425.0 | 13600 | 1.1834 | {'f1': 0.8910103420843277} | {'accuracy': 0.8904} |
| 0.0 | 426.0 | 13632 | 1.1893 | {'f1': 0.8905750798722045} | {'accuracy': 0.8904} |
| 0.0 | 427.0 | 13664 | 1.1982 | {'f1': 0.891025641025641} | {'accuracy': 0.8912} |
| 0.0 | 428.0 | 13696 | 1.1986 | {'f1': 0.891025641025641} | {'accuracy': 0.8912} |
| 0.0 | 429.0 | 13728 | 1.1987 | {'f1': 0.891025641025641} | {'accuracy': 0.8912} |
| 0.0 | 430.0 | 13760 | 1.1988 | {'f1': 0.891025641025641} | {'accuracy': 0.8912} |
| 0.0 | 431.0 | 13792 | 1.1990 | {'f1': 0.891025641025641} | {'accuracy': 0.8912} |
| 0.0 | 432.0 | 13824 | 1.1991 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 433.0 | 13856 | 1.1987 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 434.0 | 13888 | 1.1988 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 435.0 | 13920 | 1.1990 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 436.0 | 13952 | 1.1992 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 437.0 | 13984 | 1.1993 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 438.0 | 14016 | 1.1994 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 439.0 | 14048 | 1.1995 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 440.0 | 14080 | 1.1996 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 441.0 | 14112 | 1.1997 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 442.0 | 14144 | 1.2000 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 443.0 | 14176 | 1.2001 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 444.0 | 14208 | 1.2001 | {'f1': 0.8906688025630758} | {'accuracy': 0.8908} |
| 0.0 | 445.0 | 14240 | 1.2001 | {'f1': 0.8903122497998398} | {'accuracy': 0.8904} |
| 0.0 | 446.0 | 14272 | 1.2669 | {'f1': 0.8821603927986906} | {'accuracy': 0.8848} |
| 0.0 | 447.0 | 14304 | 1.3329 | {'f1': 0.8768595041322313} | {'accuracy': 0.8808} |
| 0.0 | 448.0 | 14336 | 1.3344 | {'f1': 0.8768595041322313} | {'accuracy': 0.8808} |
| 0.0 | 449.0 | 14368 | 1.3297 | {'f1': 0.8787128712871287} | {'accuracy': 0.8824} |
| 0.0 | 450.0 | 14400 | 1.3272 | {'f1': 0.8791752577319588} | {'accuracy': 0.8828} |
| 0.0 | 451.0 | 14432 | 1.3263 | {'f1': 0.8791752577319588} | {'accuracy': 0.8828} |
| 0.0 | 452.0 | 14464 | 1.3250 | {'f1': 0.8788128606760098} | {'accuracy': 0.8824} |
| 0.0 | 453.0 | 14496 | 1.3243 | {'f1': 0.8792748248866915} | {'accuracy': 0.8828} |
| 0.0 | 454.0 | 14528 | 1.3203 | {'f1': 0.8806584362139918} | {'accuracy': 0.884} |
| 0.0 | 455.0 | 14560 | 1.3188 | {'f1': 0.8806584362139918} | {'accuracy': 0.884} |
| 0.0 | 456.0 | 14592 | 1.3106 | {'f1': 0.8812166050143855} | {'accuracy': 0.8844} |
| 0.0 | 457.0 | 14624 | 1.3076 | {'f1': 0.8812166050143855} | {'accuracy': 0.8844} |
| 0.0 | 458.0 | 14656 | 1.3068 | {'f1': 0.8812166050143855} | {'accuracy': 0.8844} |
| 0.0 | 459.0 | 14688 | 1.3061 | {'f1': 0.8812166050143855} | {'accuracy': 0.8844} |
| 0.0 | 460.0 | 14720 | 1.3026 | {'f1': 0.8804928131416838} | {'accuracy': 0.8836} |
| 0.0 | 461.0 | 14752 | 1.3008 | {'f1': 0.8809523809523809} | {'accuracy': 0.884} |
| 0.0 | 462.0 | 14784 | 1.3000 | {'f1': 0.8809523809523809} | {'accuracy': 0.884} |
| 0.0 | 463.0 | 14816 | 1.2993 | {'f1': 0.8809523809523809} | {'accuracy': 0.884} |
| 0.0 | 464.0 | 14848 | 1.2959 | {'f1': 0.8809523809523809} | {'accuracy': 0.884} |
| 0.0 | 465.0 | 14880 | 1.2951 | {'f1': 0.8809523809523809} | {'accuracy': 0.884} |
| 0.0 | 466.0 | 14912 | 1.2948 | {'f1': 0.8809523809523809} | {'accuracy': 0.884} |
| 0.0 | 467.0 | 14944 | 1.2941 | {'f1': 0.8805908904390645} | {'accuracy': 0.8836} |
| 0.0 | 468.0 | 14976 | 1.2933 | {'f1': 0.8805908904390645} | {'accuracy': 0.8836} |
| 0.0 | 469.0 | 15008 | 1.2930 | {'f1': 0.8805908904390645} | {'accuracy': 0.8836} |
| 0.0 | 470.0 | 15040 | 1.2729 | {'f1': 0.8841761827079934} | {'accuracy': 0.8864} |
| 0.0 | 471.0 | 15072 | 1.2600 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 472.0 | 15104 | 1.2595 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 473.0 | 15136 | 1.2593 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 474.0 | 15168 | 1.2594 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 475.0 | 15200 | 1.2592 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 476.0 | 15232 | 1.2584 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 477.0 | 15264 | 1.2578 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 478.0 | 15296 | 1.2578 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 479.0 | 15328 | 1.2577 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 480.0 | 15360 | 1.2578 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 481.0 | 15392 | 1.2575 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 482.0 | 15424 | 1.2575 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 483.0 | 15456 | 1.2574 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 484.0 | 15488 | 1.2574 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 485.0 | 15520 | 1.2596 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 486.0 | 15552 | 1.2595 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 487.0 | 15584 | 1.2592 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 488.0 | 15616 | 1.2589 | {'f1': 0.8853658536585366} | {'accuracy': 0.8872} |
| 0.0 | 489.0 | 15648 | 1.2589 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 490.0 | 15680 | 1.2588 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 491.0 | 15712 | 1.2588 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 492.0 | 15744 | 1.2584 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 493.0 | 15776 | 1.2580 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 494.0 | 15808 | 1.2580 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 495.0 | 15840 | 1.2566 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 496.0 | 15872 | 1.2564 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 497.0 | 15904 | 1.2564 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 498.0 | 15936 | 1.2564 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 499.0 | 15968 | 1.2564 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
| 0.0 | 500.0 | 16000 | 1.2555 | {'f1': 0.8858187728565624} | {'accuracy': 0.8876} |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
