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T5tuned

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bert
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resume-bert

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  1. README.md +80 -154
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5626
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- - Accuracy: 0.8360
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- - F1: 0.7230
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- - Precision: 0.7553
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- - Recall: 0.7035
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  ## Model description
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@@ -44,8 +44,8 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 16
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- - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
@@ -57,153 +57,79 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | 1.5247 | 0.02 | 50 | 1.3656 | 0.5365 | 0.1841 | 0.1839 | 0.2054 |
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- | 1.3764 | 0.04 | 100 | 1.2543 | 0.5539 | 0.2163 | 0.2377 | 0.2461 |
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- | 1.3046 | 0.06 | 150 | 1.5517 | 0.4440 | 0.2151 | 0.2231 | 0.2624 |
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- | 1.2523 | 0.08 | 200 | 1.2396 | 0.5535 | 0.2195 | 0.4997 | 0.2627 |
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- | 1.1098 | 0.1 | 250 | 1.0067 | 0.6573 | 0.3306 | 0.5386 | 0.3212 |
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- | 1.0741 | 0.12 | 300 | 1.0024 | 0.6414 | 0.3577 | 0.5714 | 0.3656 |
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- | 1.0024 | 0.14 | 350 | 0.9799 | 0.7002 | 0.4266 | 0.5953 | 0.4209 |
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- | 1.0388 | 0.16 | 400 | 0.9474 | 0.7050 | 0.4228 | 0.5023 | 0.4189 |
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- | 0.9636 | 0.18 | 450 | 0.8516 | 0.7154 | 0.4555 | 0.5558 | 0.4595 |
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- | 0.9631 | 0.2 | 500 | 0.8184 | 0.7273 | 0.4893 | 0.6215 | 0.4590 |
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- | 0.8994 | 0.22 | 550 | 0.8795 | 0.7371 | 0.5013 | 0.6266 | 0.4755 |
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- | 0.9249 | 0.24 | 600 | 0.8099 | 0.7503 | 0.5343 | 0.6028 | 0.5132 |
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- | 0.8182 | 0.26 | 650 | 0.7670 | 0.7454 | 0.5381 | 0.5897 | 0.5362 |
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- | 0.8872 | 0.28 | 700 | 0.7848 | 0.7471 | 0.5722 | 0.6469 | 0.5761 |
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- | 0.8227 | 0.31 | 750 | 0.8970 | 0.7366 | 0.5019 | 0.6595 | 0.4832 |
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- | 0.7964 | 0.33 | 800 | 0.7660 | 0.7523 | 0.5409 | 0.5435 | 0.5837 |
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- | 0.7897 | 0.35 | 850 | 0.9406 | 0.7072 | 0.5390 | 0.6241 | 0.5189 |
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- | 0.8045 | 0.37 | 900 | 0.8252 | 0.7215 | 0.4806 | 0.6539 | 0.4352 |
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- | 0.7349 | 0.39 | 950 | 0.7106 | 0.7828 | 0.6034 | 0.6272 | 0.5884 |
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- | 0.7794 | 0.41 | 1000 | 0.6791 | 0.7837 | 0.5893 | 0.6250 | 0.5803 |
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- | 0.7159 | 0.43 | 1050 | 0.6934 | 0.7842 | 0.5837 | 0.6654 | 0.5587 |
81
- | 0.7128 | 0.45 | 1100 | 0.7069 | 0.7843 | 0.6076 | 0.6533 | 0.5776 |
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- | 0.7849 | 0.47 | 1150 | 0.7099 | 0.7620 | 0.5944 | 0.7678 | 0.5965 |
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- | 0.741 | 0.49 | 1200 | 0.7663 | 0.7478 | 0.5749 | 0.7549 | 0.5704 |
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- | 0.6905 | 0.51 | 1250 | 0.6842 | 0.7925 | 0.6148 | 0.6396 | 0.6041 |
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- | 0.7195 | 0.53 | 1300 | 0.7248 | 0.7720 | 0.5769 | 0.7638 | 0.5497 |
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- | 0.7394 | 0.55 | 1350 | 0.6870 | 0.7911 | 0.6002 | 0.6628 | 0.5739 |
87
- | 0.6696 | 0.57 | 1400 | 0.6674 | 0.7987 | 0.6290 | 0.6450 | 0.6199 |
88
- | 0.7133 | 0.59 | 1450 | 0.6785 | 0.7938 | 0.6141 | 0.6470 | 0.6134 |
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- | 0.6743 | 0.61 | 1500 | 0.6901 | 0.7965 | 0.6184 | 0.8136 | 0.5925 |
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- | 0.684 | 0.63 | 1550 | 0.6921 | 0.7957 | 0.6297 | 0.6979 | 0.6063 |
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- | 0.6555 | 0.65 | 1600 | 0.7061 | 0.7790 | 0.6010 | 0.6025 | 0.6244 |
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- | 0.6188 | 0.67 | 1650 | 0.7503 | 0.7781 | 0.5902 | 0.8093 | 0.5338 |
93
- | 0.7457 | 0.69 | 1700 | 0.6710 | 0.7978 | 0.6026 | 0.6432 | 0.6066 |
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- | 0.7393 | 0.71 | 1750 | 0.6759 | 0.7930 | 0.6339 | 0.7666 | 0.6475 |
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- | 0.7628 | 0.73 | 1800 | 0.6377 | 0.8089 | 0.6456 | 0.6942 | 0.6522 |
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- | 0.735 | 0.75 | 1850 | 0.7434 | 0.7930 | 0.6283 | 0.6680 | 0.6121 |
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- | 0.7296 | 0.77 | 1900 | 0.6502 | 0.8126 | 0.6487 | 0.7385 | 0.6379 |
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- | 0.6928 | 0.79 | 1950 | 0.6253 | 0.8136 | 0.6511 | 0.7353 | 0.6320 |
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- | 0.6352 | 0.81 | 2000 | 0.6476 | 0.8059 | 0.6374 | 0.8051 | 0.6263 |
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- | 0.6468 | 0.83 | 2050 | 0.6562 | 0.8032 | 0.6314 | 0.7535 | 0.6204 |
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- | 0.7292 | 0.85 | 2100 | 0.6385 | 0.7957 | 0.5927 | 0.6855 | 0.5790 |
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- | 0.6161 | 0.87 | 2150 | 0.6428 | 0.8056 | 0.6205 | 0.6775 | 0.6026 |
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- | 0.6515 | 0.89 | 2200 | 0.6184 | 0.8162 | 0.6405 | 0.6590 | 0.6361 |
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- | 0.6213 | 0.92 | 2250 | 0.6490 | 0.8047 | 0.6320 | 0.6843 | 0.6086 |
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- | 0.6625 | 0.94 | 2300 | 0.7454 | 0.7734 | 0.5984 | 0.6586 | 0.6370 |
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- | 0.698 | 0.96 | 2350 | 0.7369 | 0.7873 | 0.6150 | 0.7827 | 0.5866 |
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- | 0.6565 | 0.98 | 2400 | 0.6749 | 0.7957 | 0.6368 | 0.7346 | 0.6125 |
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- | 0.7032 | 1.0 | 2450 | 0.6655 | 0.8008 | 0.6351 | 0.6600 | 0.6236 |
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- | 0.5442 | 1.02 | 2500 | 0.6429 | 0.8187 | 0.6571 | 0.7666 | 0.6432 |
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- | 0.6461 | 1.04 | 2550 | 0.6369 | 0.8037 | 0.6342 | 0.7544 | 0.6066 |
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- | 0.5382 | 1.06 | 2600 | 0.6912 | 0.8069 | 0.6448 | 0.6517 | 0.6407 |
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- | 0.5253 | 1.08 | 2650 | 0.7129 | 0.8041 | 0.6166 | 0.8399 | 0.5795 |
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- | 0.5729 | 1.1 | 2700 | 0.7291 | 0.7814 | 0.6351 | 0.6685 | 0.6547 |
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- | 0.6183 | 1.12 | 2750 | 0.6339 | 0.8145 | 0.6687 | 0.7169 | 0.6531 |
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- | 0.5461 | 1.14 | 2800 | 0.6108 | 0.8176 | 0.6838 | 0.7399 | 0.6695 |
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- | 0.5827 | 1.16 | 2850 | 0.6113 | 0.8182 | 0.6759 | 0.7503 | 0.6471 |
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- | 0.5903 | 1.18 | 2900 | 0.6881 | 0.8022 | 0.6551 | 0.7410 | 0.6453 |
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- | 0.5672 | 1.2 | 2950 | 0.5965 | 0.8214 | 0.6741 | 0.7804 | 0.6591 |
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- | 0.543 | 1.22 | 3000 | 0.6554 | 0.8164 | 0.6557 | 0.7584 | 0.6656 |
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- | 0.6311 | 1.24 | 3050 | 0.6137 | 0.8219 | 0.6840 | 0.7789 | 0.6486 |
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- | 0.661 | 1.26 | 3100 | 0.6244 | 0.8184 | 0.6805 | 0.7788 | 0.6517 |
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- | 0.5055 | 1.28 | 3150 | 0.6356 | 0.8145 | 0.6768 | 0.7629 | 0.6542 |
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- | 0.4951 | 1.3 | 3200 | 0.6167 | 0.8175 | 0.6770 | 0.7676 | 0.6644 |
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- | 0.5633 | 1.32 | 3250 | 0.6051 | 0.8232 | 0.6655 | 0.7882 | 0.6432 |
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- | 0.551 | 1.34 | 3300 | 0.6193 | 0.8211 | 0.6860 | 0.7320 | 0.6629 |
126
- | 0.5962 | 1.36 | 3350 | 0.6165 | 0.8087 | 0.6533 | 0.7449 | 0.6251 |
127
- | 0.5257 | 1.38 | 3400 | 0.5966 | 0.8193 | 0.6935 | 0.7627 | 0.6739 |
128
- | 0.5366 | 1.4 | 3450 | 0.6110 | 0.8198 | 0.6911 | 0.7669 | 0.6519 |
129
- | 0.5844 | 1.42 | 3500 | 0.6151 | 0.8223 | 0.6760 | 0.7847 | 0.6455 |
130
- | 0.5652 | 1.44 | 3550 | 0.5907 | 0.8252 | 0.6723 | 0.7723 | 0.6646 |
131
- | 0.5488 | 1.46 | 3600 | 0.6074 | 0.8268 | 0.7047 | 0.7835 | 0.6759 |
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- | 0.5235 | 1.48 | 3650 | 0.6133 | 0.8142 | 0.6850 | 0.7856 | 0.6568 |
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- | 0.5418 | 1.5 | 3700 | 0.6413 | 0.8215 | 0.6872 | 0.7915 | 0.6452 |
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- | 0.5564 | 1.53 | 3750 | 0.5809 | 0.8286 | 0.7049 | 0.7748 | 0.6855 |
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- | 0.5976 | 1.55 | 3800 | 0.5913 | 0.8244 | 0.6979 | 0.7594 | 0.6806 |
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- | 0.5032 | 1.57 | 3850 | 0.6211 | 0.8250 | 0.6663 | 0.7811 | 0.6485 |
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- | 0.535 | 1.59 | 3900 | 0.5805 | 0.8287 | 0.7001 | 0.7859 | 0.6694 |
138
- | 0.5223 | 1.61 | 3950 | 0.6010 | 0.8189 | 0.6861 | 0.7607 | 0.6813 |
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- | 0.4967 | 1.63 | 4000 | 0.6011 | 0.8295 | 0.7019 | 0.7836 | 0.6717 |
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- | 0.507 | 1.65 | 4050 | 0.6121 | 0.8196 | 0.7075 | 0.7632 | 0.6866 |
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- | 0.585 | 1.67 | 4100 | 0.6019 | 0.8235 | 0.6669 | 0.7633 | 0.6364 |
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- | 0.5733 | 1.69 | 4150 | 0.5797 | 0.8302 | 0.6892 | 0.7955 | 0.6579 |
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- | 0.5482 | 1.71 | 4200 | 0.5895 | 0.8282 | 0.6960 | 0.7557 | 0.6862 |
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- | 0.5603 | 1.73 | 4250 | 0.5730 | 0.8270 | 0.7211 | 0.7751 | 0.6974 |
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- | 0.5017 | 1.75 | 4300 | 0.5956 | 0.8310 | 0.7061 | 0.7879 | 0.6721 |
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- | 0.5655 | 1.77 | 4350 | 0.5619 | 0.8326 | 0.7107 | 0.7976 | 0.6725 |
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- | 0.5659 | 1.79 | 4400 | 0.6281 | 0.8125 | 0.7087 | 0.7859 | 0.6691 |
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- | 0.5058 | 1.81 | 4450 | 0.5696 | 0.8307 | 0.7146 | 0.7723 | 0.6985 |
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- | 0.5106 | 1.83 | 4500 | 0.5951 | 0.8189 | 0.7095 | 0.7160 | 0.7131 |
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- | 0.5845 | 1.85 | 4550 | 0.5668 | 0.8336 | 0.7136 | 0.8014 | 0.6853 |
151
- | 0.5256 | 1.87 | 4600 | 0.5658 | 0.8295 | 0.7087 | 0.7588 | 0.6973 |
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- | 0.5136 | 1.89 | 4650 | 0.5933 | 0.8300 | 0.6825 | 0.7629 | 0.6743 |
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- | 0.5515 | 1.91 | 4700 | 0.5753 | 0.8175 | 0.6839 | 0.8091 | 0.6319 |
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- | 0.5548 | 1.93 | 4750 | 0.5473 | 0.8346 | 0.7275 | 0.7792 | 0.6979 |
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- | 0.5377 | 1.95 | 4800 | 0.5725 | 0.8302 | 0.7307 | 0.7563 | 0.7166 |
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- | 0.5204 | 1.97 | 4850 | 0.5768 | 0.8288 | 0.6997 | 0.7873 | 0.6671 |
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- | 0.5688 | 1.99 | 4900 | 0.5480 | 0.8361 | 0.7244 | 0.8019 | 0.6887 |
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- | 0.4596 | 2.01 | 4950 | 0.6084 | 0.8298 | 0.7231 | 0.7653 | 0.7014 |
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- | 0.4357 | 2.03 | 5000 | 0.6180 | 0.8333 | 0.7251 | 0.7579 | 0.7046 |
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- | 0.4787 | 2.05 | 5050 | 0.5744 | 0.8293 | 0.7216 | 0.7789 | 0.6925 |
161
- | 0.5183 | 2.07 | 5100 | 0.5747 | 0.8299 | 0.7263 | 0.7687 | 0.7092 |
162
- | 0.532 | 2.09 | 5150 | 0.5626 | 0.8308 | 0.7150 | 0.7873 | 0.6920 |
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- | 0.4789 | 2.11 | 5200 | 0.5659 | 0.8308 | 0.7297 | 0.7603 | 0.7215 |
164
- | 0.5121 | 2.14 | 5250 | 0.5739 | 0.8329 | 0.7229 | 0.7850 | 0.6880 |
165
- | 0.4516 | 2.16 | 5300 | 0.5592 | 0.8376 | 0.7306 | 0.7966 | 0.6999 |
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- | 0.4789 | 2.18 | 5350 | 0.5679 | 0.8329 | 0.7232 | 0.7427 | 0.7122 |
167
- | 0.4191 | 2.2 | 5400 | 0.5953 | 0.8282 | 0.7331 | 0.7701 | 0.7203 |
168
- | 0.4519 | 2.22 | 5450 | 0.5779 | 0.8319 | 0.7233 | 0.7727 | 0.7047 |
169
- | 0.4544 | 2.24 | 5500 | 0.5890 | 0.8330 | 0.7262 | 0.7535 | 0.7208 |
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- | 0.4191 | 2.26 | 5550 | 0.5872 | 0.8356 | 0.7307 | 0.7909 | 0.6951 |
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- | 0.459 | 2.28 | 5600 | 0.5952 | 0.8274 | 0.7241 | 0.7376 | 0.7178 |
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- | 0.4666 | 2.3 | 5650 | 0.5940 | 0.8310 | 0.7151 | 0.7634 | 0.7057 |
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- | 0.4608 | 2.32 | 5700 | 0.6021 | 0.8324 | 0.7202 | 0.7683 | 0.7026 |
174
- | 0.4022 | 2.34 | 5750 | 0.5873 | 0.8346 | 0.7289 | 0.7705 | 0.7072 |
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- | 0.4588 | 2.36 | 5800 | 0.5611 | 0.8327 | 0.7271 | 0.7769 | 0.7070 |
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- | 0.3523 | 2.38 | 5850 | 0.5999 | 0.8370 | 0.7255 | 0.7761 | 0.7029 |
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- | 0.422 | 2.4 | 5900 | 0.5940 | 0.8367 | 0.7239 | 0.7769 | 0.7047 |
178
- | 0.4827 | 2.42 | 5950 | 0.6002 | 0.8368 | 0.7194 | 0.7864 | 0.6945 |
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- | 0.4287 | 2.44 | 6000 | 0.5737 | 0.8380 | 0.7206 | 0.7678 | 0.7080 |
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- | 0.3921 | 2.46 | 6050 | 0.5859 | 0.8334 | 0.7258 | 0.7612 | 0.7166 |
181
- | 0.4183 | 2.48 | 6100 | 0.5747 | 0.8400 | 0.7326 | 0.7756 | 0.7083 |
182
- | 0.3758 | 2.5 | 6150 | 0.5781 | 0.8382 | 0.7276 | 0.7611 | 0.7126 |
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- | 0.4809 | 2.52 | 6200 | 0.5657 | 0.8383 | 0.7333 | 0.7778 | 0.7055 |
184
- | 0.4405 | 2.54 | 6250 | 0.5809 | 0.8320 | 0.7345 | 0.7538 | 0.7242 |
185
- | 0.3864 | 2.56 | 6300 | 0.5704 | 0.8393 | 0.7361 | 0.7742 | 0.7175 |
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- | 0.4576 | 2.58 | 6350 | 0.5602 | 0.8404 | 0.7353 | 0.7862 | 0.7098 |
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- | 0.4447 | 2.6 | 6400 | 0.5542 | 0.8391 | 0.7365 | 0.7695 | 0.7183 |
188
- | 0.4523 | 2.62 | 6450 | 0.5484 | 0.8384 | 0.7396 | 0.7802 | 0.7149 |
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- | 0.456 | 2.64 | 6500 | 0.5608 | 0.8392 | 0.7351 | 0.7816 | 0.7123 |
190
- | 0.4648 | 2.66 | 6550 | 0.5637 | 0.8394 | 0.7364 | 0.7808 | 0.7107 |
191
- | 0.3735 | 2.68 | 6600 | 0.5752 | 0.8377 | 0.7385 | 0.7749 | 0.7213 |
192
- | 0.4042 | 2.7 | 6650 | 0.5647 | 0.8361 | 0.7322 | 0.7790 | 0.7134 |
193
- | 0.425 | 2.72 | 6700 | 0.5722 | 0.8380 | 0.7364 | 0.7829 | 0.7090 |
194
- | 0.3668 | 2.75 | 6750 | 0.5900 | 0.8391 | 0.7363 | 0.7693 | 0.7204 |
195
- | 0.4614 | 2.77 | 6800 | 0.5616 | 0.8396 | 0.7364 | 0.7779 | 0.7158 |
196
- | 0.4351 | 2.79 | 6850 | 0.5634 | 0.8390 | 0.7359 | 0.7657 | 0.7220 |
197
- | 0.4008 | 2.81 | 6900 | 0.5679 | 0.8388 | 0.7354 | 0.7716 | 0.7168 |
198
- | 0.4538 | 2.83 | 6950 | 0.5610 | 0.8366 | 0.7425 | 0.7593 | 0.7350 |
199
- | 0.3839 | 2.85 | 7000 | 0.5657 | 0.8404 | 0.7376 | 0.7820 | 0.7142 |
200
- | 0.424 | 2.87 | 7050 | 0.5595 | 0.8395 | 0.7399 | 0.7754 | 0.7217 |
201
- | 0.4125 | 2.89 | 7100 | 0.5581 | 0.8382 | 0.7411 | 0.7622 | 0.7301 |
202
- | 0.3748 | 2.91 | 7150 | 0.5620 | 0.8388 | 0.7411 | 0.7660 | 0.7270 |
203
- | 0.3782 | 2.93 | 7200 | 0.5601 | 0.8394 | 0.7421 | 0.7689 | 0.7266 |
204
- | 0.4413 | 2.95 | 7250 | 0.5559 | 0.8396 | 0.7426 | 0.7680 | 0.7280 |
205
- | 0.4182 | 2.97 | 7300 | 0.5530 | 0.8400 | 0.7429 | 0.7692 | 0.7274 |
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- | 0.4383 | 2.99 | 7350 | 0.5519 | 0.8405 | 0.7438 | 0.7715 | 0.7276 |
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.5372
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+ - Accuracy: 0.8371
25
+ - F1: 0.7316
26
+ - Precision: 0.7615
27
+ - Recall: 0.7112
28
 
29
  ## Model description
30
 
 
44
 
45
  The following hyperparameters were used during training:
46
  - learning_rate: 5e-05
47
+ - train_batch_size: 32
48
+ - eval_batch_size: 64
49
  - seed: 42
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
 
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58
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
59
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 1.4792 | 0.04 | 50 | 1.3375 | 0.5157 | 0.1525 | 0.2666 | 0.2006 |
61
+ | 1.2938 | 0.08 | 100 | 1.1358 | 0.6112 | 0.2290 | 0.4100 | 0.2395 |
62
+ | 1.1393 | 0.12 | 150 | 1.0186 | 0.6552 | 0.4235 | 0.5382 | 0.4140 |
63
+ | 1.0714 | 0.16 | 200 | 0.9367 | 0.7043 | 0.4586 | 0.5566 | 0.4520 |
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+ | 0.9874 | 0.2 | 250 | 0.8549 | 0.7151 | 0.4912 | 0.7129 | 0.4745 |
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+ | 0.8875 | 0.24 | 300 | 0.7741 | 0.7479 | 0.5512 | 0.6722 | 0.5300 |
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+ | 0.8267 | 0.28 | 350 | 0.7463 | 0.7497 | 0.5841 | 0.6718 | 0.5784 |
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+ | 0.798 | 0.33 | 400 | 0.7388 | 0.7559 | 0.5798 | 0.6802 | 0.5712 |
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+ | 0.778 | 0.37 | 450 | 0.7351 | 0.7668 | 0.5795 | 0.7799 | 0.5318 |
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+ | 0.7568 | 0.41 | 500 | 0.7147 | 0.7792 | 0.5958 | 0.7228 | 0.5931 |
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+ | 0.721 | 0.45 | 550 | 0.8179 | 0.7299 | 0.5823 | 0.6824 | 0.5868 |
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+ | 0.7534 | 0.49 | 600 | 0.6631 | 0.7874 | 0.6106 | 0.7809 | 0.5804 |
72
+ | 0.7242 | 0.53 | 650 | 0.6918 | 0.7843 | 0.5966 | 0.7648 | 0.5666 |
73
+ | 0.7236 | 0.57 | 700 | 0.7457 | 0.7733 | 0.5752 | 0.7704 | 0.5465 |
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+ | 0.702 | 0.61 | 750 | 0.6810 | 0.7928 | 0.6059 | 0.7791 | 0.5692 |
75
+ | 0.6681 | 0.65 | 800 | 0.6318 | 0.8029 | 0.6441 | 0.7699 | 0.6255 |
76
+ | 0.6463 | 0.69 | 850 | 0.6754 | 0.7968 | 0.6387 | 0.7553 | 0.6412 |
77
+ | 0.7443 | 0.73 | 900 | 0.7580 | 0.7720 | 0.5804 | 0.7278 | 0.5933 |
78
+ | 0.7273 | 0.77 | 950 | 0.6410 | 0.8082 | 0.6364 | 0.7804 | 0.6112 |
79
+ | 0.6329 | 0.81 | 1000 | 0.6294 | 0.8028 | 0.6452 | 0.7305 | 0.6362 |
80
+ | 0.6623 | 0.85 | 1050 | 0.6742 | 0.7797 | 0.5614 | 0.8038 | 0.5437 |
81
+ | 0.6198 | 0.89 | 1100 | 0.6250 | 0.8048 | 0.6306 | 0.7658 | 0.6202 |
82
+ | 0.614 | 0.93 | 1150 | 0.7138 | 0.7852 | 0.6231 | 0.6792 | 0.6433 |
83
+ | 0.6423 | 0.98 | 1200 | 0.6581 | 0.7991 | 0.6601 | 0.7570 | 0.6526 |
84
+ | 0.6175 | 1.02 | 1250 | 0.6336 | 0.8107 | 0.6790 | 0.7297 | 0.6737 |
85
+ | 0.5583 | 1.06 | 1300 | 0.6364 | 0.8074 | 0.6505 | 0.7822 | 0.6284 |
86
+ | 0.5371 | 1.1 | 1350 | 0.6051 | 0.8158 | 0.6878 | 0.7743 | 0.6513 |
87
+ | 0.5173 | 1.14 | 1400 | 0.6464 | 0.7972 | 0.6981 | 0.7249 | 0.7007 |
88
+ | 0.5602 | 1.18 | 1450 | 0.6631 | 0.7928 | 0.6419 | 0.7471 | 0.6506 |
89
+ | 0.5187 | 1.22 | 1500 | 0.6140 | 0.8164 | 0.6756 | 0.7739 | 0.6674 |
90
+ | 0.6183 | 1.26 | 1550 | 0.6166 | 0.8170 | 0.6782 | 0.7513 | 0.6611 |
91
+ | 0.4991 | 1.3 | 1600 | 0.6289 | 0.8198 | 0.6920 | 0.8022 | 0.6470 |
92
+ | 0.5449 | 1.34 | 1650 | 0.6011 | 0.8194 | 0.6995 | 0.7613 | 0.6684 |
93
+ | 0.5358 | 1.38 | 1700 | 0.6036 | 0.8110 | 0.7041 | 0.7335 | 0.6973 |
94
+ | 0.5451 | 1.42 | 1750 | 0.6156 | 0.8141 | 0.6392 | 0.8173 | 0.6160 |
95
+ | 0.5421 | 1.46 | 1800 | 0.5723 | 0.8252 | 0.7149 | 0.7704 | 0.6930 |
96
+ | 0.5199 | 1.5 | 1850 | 0.6290 | 0.8129 | 0.6658 | 0.7989 | 0.6102 |
97
+ | 0.5477 | 1.54 | 1900 | 0.5792 | 0.8222 | 0.7008 | 0.7831 | 0.6682 |
98
+ | 0.5117 | 1.59 | 1950 | 0.5652 | 0.8288 | 0.7119 | 0.7801 | 0.6796 |
99
+ | 0.5201 | 1.63 | 2000 | 0.5661 | 0.8276 | 0.7143 | 0.7802 | 0.6871 |
100
+ | 0.5098 | 1.67 | 2050 | 0.5745 | 0.8265 | 0.6906 | 0.7897 | 0.6591 |
101
+ | 0.5226 | 1.71 | 2100 | 0.5768 | 0.8251 | 0.6948 | 0.7516 | 0.6903 |
102
+ | 0.5367 | 1.75 | 2150 | 0.5573 | 0.8318 | 0.7180 | 0.7886 | 0.6879 |
103
+ | 0.5484 | 1.79 | 2200 | 0.5738 | 0.8241 | 0.6990 | 0.7818 | 0.6638 |
104
+ | 0.534 | 1.83 | 2250 | 0.5601 | 0.8299 | 0.7167 | 0.7799 | 0.6898 |
105
+ | 0.5423 | 1.87 | 2300 | 0.5571 | 0.8240 | 0.7228 | 0.7592 | 0.7153 |
106
+ | 0.5056 | 1.91 | 2350 | 0.5635 | 0.8267 | 0.7004 | 0.8005 | 0.6642 |
107
+ | 0.5355 | 1.95 | 2400 | 0.5546 | 0.8275 | 0.7167 | 0.7681 | 0.7053 |
108
+ | 0.5387 | 1.99 | 2450 | 0.5417 | 0.8315 | 0.7277 | 0.7656 | 0.7028 |
109
+ | 0.4148 | 2.03 | 2500 | 0.6051 | 0.8310 | 0.7170 | 0.7716 | 0.6878 |
110
+ | 0.4685 | 2.07 | 2550 | 0.5605 | 0.8302 | 0.7139 | 0.7818 | 0.6980 |
111
+ | 0.5007 | 2.11 | 2600 | 0.5530 | 0.8326 | 0.7288 | 0.7650 | 0.7165 |
112
+ | 0.4524 | 2.15 | 2650 | 0.5648 | 0.8302 | 0.7188 | 0.7680 | 0.6941 |
113
+ | 0.4437 | 2.2 | 2700 | 0.5636 | 0.8275 | 0.7287 | 0.7684 | 0.7171 |
114
+ | 0.4326 | 2.24 | 2750 | 0.5542 | 0.8341 | 0.7166 | 0.7889 | 0.6903 |
115
+ | 0.4182 | 2.28 | 2800 | 0.5697 | 0.8272 | 0.7283 | 0.7398 | 0.7227 |
116
+ | 0.4466 | 2.32 | 2850 | 0.5628 | 0.8343 | 0.7257 | 0.7925 | 0.6958 |
117
+ | 0.4118 | 2.36 | 2900 | 0.5717 | 0.8266 | 0.7249 | 0.7334 | 0.7250 |
118
+ | 0.3689 | 2.4 | 2950 | 0.5716 | 0.8342 | 0.7259 | 0.7705 | 0.7105 |
119
+ | 0.4332 | 2.44 | 3000 | 0.5557 | 0.8345 | 0.7316 | 0.7586 | 0.7192 |
120
+ | 0.3926 | 2.48 | 3050 | 0.5635 | 0.8352 | 0.7266 | 0.7762 | 0.7071 |
121
+ | 0.4141 | 2.52 | 3100 | 0.5553 | 0.8354 | 0.7273 | 0.7732 | 0.6983 |
122
+ | 0.3984 | 2.56 | 3150 | 0.5605 | 0.8349 | 0.7343 | 0.7670 | 0.7142 |
123
+ | 0.4267 | 2.6 | 3200 | 0.5478 | 0.8376 | 0.7325 | 0.7828 | 0.7054 |
124
+ | 0.4309 | 2.64 | 3250 | 0.5512 | 0.8339 | 0.7341 | 0.7672 | 0.7155 |
125
+ | 0.408 | 2.68 | 3300 | 0.5598 | 0.8351 | 0.7339 | 0.7637 | 0.7134 |
126
+ | 0.4174 | 2.72 | 3350 | 0.5553 | 0.8320 | 0.7374 | 0.7668 | 0.7206 |
127
+ | 0.3979 | 2.76 | 3400 | 0.5559 | 0.8357 | 0.7342 | 0.7713 | 0.7151 |
128
+ | 0.4021 | 2.8 | 3450 | 0.5500 | 0.8356 | 0.7364 | 0.7595 | 0.7259 |
129
+ | 0.4018 | 2.85 | 3500 | 0.5485 | 0.8371 | 0.7356 | 0.7715 | 0.7151 |
130
+ | 0.392 | 2.89 | 3550 | 0.5566 | 0.8348 | 0.7368 | 0.7627 | 0.7252 |
131
+ | 0.3695 | 2.93 | 3600 | 0.5548 | 0.8355 | 0.7380 | 0.7614 | 0.7251 |
132
+ | 0.3936 | 2.97 | 3650 | 0.5503 | 0.8353 | 0.7387 | 0.7627 | 0.7256 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
133
 
134
 
135
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