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roberta-large-ner-qlorafinetune-runs-colab

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@@ -23,11 +23,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0698
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- - Precision: 0.9379
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- - Recall: 0.9598
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- - F1: 0.9487
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- - Accuracy: 0.9815
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  ## Model description
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@@ -47,140 +47,79 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0004
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - training_steps: 2448
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 2.47 | 0.0327 | 20 | 1.0383 | 0.6206 | 0.2042 | 0.3073 | 0.7573 |
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- | 0.9608 | 0.0654 | 40 | 0.6153 | 0.6478 | 0.5029 | 0.5662 | 0.8509 |
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- | 0.5769 | 0.0980 | 60 | 0.3786 | 0.7804 | 0.6937 | 0.7345 | 0.8990 |
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- | 0.4141 | 0.1307 | 80 | 0.2461 | 0.7956 | 0.8354 | 0.8150 | 0.9333 |
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- | 0.3257 | 0.1634 | 100 | 0.2534 | 0.7987 | 0.8565 | 0.8266 | 0.9387 |
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- | 0.3135 | 0.1961 | 120 | 0.1785 | 0.8271 | 0.8810 | 0.8532 | 0.9490 |
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- | 0.27 | 0.2288 | 140 | 0.1748 | 0.8318 | 0.8876 | 0.8588 | 0.9492 |
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- | 0.216 | 0.2614 | 160 | 0.1584 | 0.8993 | 0.8667 | 0.8827 | 0.9526 |
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- | 0.23 | 0.2941 | 180 | 0.1451 | 0.8812 | 0.8902 | 0.8857 | 0.9583 |
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- | 0.1933 | 0.3268 | 200 | 0.1347 | 0.8794 | 0.8871 | 0.8833 | 0.9608 |
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- | 0.2051 | 0.3595 | 220 | 0.1327 | 0.8746 | 0.9240 | 0.8986 | 0.9636 |
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- | 0.228 | 0.3922 | 240 | 0.1672 | 0.8456 | 0.9088 | 0.8761 | 0.9565 |
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- | 0.1974 | 0.4248 | 260 | 0.1228 | 0.8864 | 0.9189 | 0.9023 | 0.9631 |
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- | 0.1593 | 0.4575 | 280 | 0.1269 | 0.8786 | 0.9276 | 0.9024 | 0.9644 |
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- | 0.1433 | 0.4902 | 300 | 0.1004 | 0.9160 | 0.9281 | 0.9220 | 0.9710 |
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- | 0.145 | 0.5229 | 320 | 0.1170 | 0.8792 | 0.9138 | 0.8962 | 0.9641 |
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- | 0.1537 | 0.5556 | 340 | 0.1387 | 0.8696 | 0.9276 | 0.8977 | 0.9622 |
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- | 0.1257 | 0.5882 | 360 | 0.0976 | 0.9024 | 0.9405 | 0.9211 | 0.9712 |
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- | 0.1379 | 0.6209 | 380 | 0.1033 | 0.8999 | 0.9343 | 0.9168 | 0.9694 |
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- | 0.142 | 0.6536 | 400 | 0.0945 | 0.9116 | 0.9283 | 0.9199 | 0.9710 |
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- | 0.1345 | 0.6863 | 420 | 0.1204 | 0.8763 | 0.9591 | 0.9158 | 0.9654 |
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- | 0.1304 | 0.7190 | 440 | 0.0914 | 0.9096 | 0.9223 | 0.9159 | 0.9717 |
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- | 0.1254 | 0.7516 | 460 | 0.0938 | 0.9120 | 0.9513 | 0.9312 | 0.9742 |
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- | 0.1241 | 0.7843 | 480 | 0.1042 | 0.8961 | 0.9509 | 0.9227 | 0.9707 |
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- | 0.128 | 0.8170 | 500 | 0.0895 | 0.9155 | 0.9368 | 0.9261 | 0.9742 |
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- | 0.1157 | 0.8497 | 520 | 0.1089 | 0.8862 | 0.9506 | 0.9172 | 0.9702 |
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- | 0.1286 | 0.8824 | 540 | 0.1037 | 0.8965 | 0.9532 | 0.9240 | 0.9702 |
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- | 0.1263 | 0.9150 | 560 | 0.0990 | 0.9060 | 0.9532 | 0.9290 | 0.9719 |
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- | 0.1353 | 0.9477 | 580 | 0.0934 | 0.9025 | 0.9439 | 0.9227 | 0.9724 |
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- | 0.138 | 0.9804 | 600 | 0.0923 | 0.9285 | 0.9206 | 0.9246 | 0.9736 |
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- | 0.1312 | 1.0131 | 620 | 0.0874 | 0.9108 | 0.9510 | 0.9305 | 0.9748 |
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- | 0.0818 | 1.0458 | 640 | 0.0891 | 0.9130 | 0.9490 | 0.9306 | 0.9754 |
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- | 0.0889 | 1.0784 | 660 | 0.1058 | 0.9161 | 0.9392 | 0.9275 | 0.9707 |
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- | 0.0906 | 1.1111 | 680 | 0.0905 | 0.9057 | 0.9516 | 0.9281 | 0.9733 |
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- | 0.0937 | 1.1438 | 700 | 0.0945 | 0.9021 | 0.9637 | 0.9319 | 0.9745 |
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- | 0.1015 | 1.1765 | 720 | 0.1063 | 0.9007 | 0.9466 | 0.9231 | 0.9704 |
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- | 0.0864 | 1.2092 | 740 | 0.0793 | 0.9215 | 0.9604 | 0.9405 | 0.9789 |
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- | 0.1034 | 1.2418 | 760 | 0.0829 | 0.9191 | 0.9560 | 0.9372 | 0.9777 |
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- | 0.1206 | 1.2745 | 780 | 0.0840 | 0.9204 | 0.9352 | 0.9278 | 0.9752 |
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- | 0.086 | 1.3072 | 800 | 0.0803 | 0.9281 | 0.9499 | 0.9388 | 0.9781 |
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- | 0.0965 | 1.3399 | 820 | 0.0958 | 0.9147 | 0.9499 | 0.9319 | 0.9737 |
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- | 0.1056 | 1.3725 | 840 | 0.0769 | 0.9164 | 0.9412 | 0.9287 | 0.9763 |
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- | 0.0933 | 1.4052 | 860 | 0.0825 | 0.9148 | 0.9587 | 0.9362 | 0.9773 |
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- | 0.1007 | 1.4379 | 880 | 0.0881 | 0.9186 | 0.9585 | 0.9381 | 0.9768 |
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- | 0.1163 | 1.4706 | 900 | 0.1055 | 0.9051 | 0.9542 | 0.9290 | 0.9701 |
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- | 0.0745 | 1.5033 | 920 | 0.0819 | 0.9120 | 0.9522 | 0.9317 | 0.9751 |
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- | 0.08 | 1.5359 | 940 | 0.0846 | 0.9117 | 0.9575 | 0.9340 | 0.9759 |
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- | 0.1087 | 1.5686 | 960 | 0.0830 | 0.9201 | 0.9500 | 0.9348 | 0.9755 |
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- | 0.0892 | 1.6013 | 980 | 0.0767 | 0.9293 | 0.9589 | 0.9439 | 0.9796 |
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- | 0.0859 | 1.6340 | 1000 | 0.0894 | 0.9212 | 0.9539 | 0.9373 | 0.9764 |
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- | 0.071 | 1.6667 | 1020 | 0.0926 | 0.9221 | 0.9534 | 0.9375 | 0.9758 |
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- | 0.081 | 1.6993 | 1040 | 0.0811 | 0.9200 | 0.9549 | 0.9371 | 0.9773 |
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- | 0.0674 | 1.7320 | 1060 | 0.0910 | 0.9233 | 0.9568 | 0.9398 | 0.9757 |
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- | 0.0837 | 1.7647 | 1080 | 0.0854 | 0.9117 | 0.9458 | 0.9284 | 0.9755 |
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- | 0.089 | 1.7974 | 1100 | 0.0802 | 0.9211 | 0.9284 | 0.9248 | 0.9743 |
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- | 0.0942 | 1.8301 | 1120 | 0.0857 | 0.9167 | 0.9615 | 0.9385 | 0.9772 |
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- | 0.0792 | 1.8627 | 1140 | 0.0751 | 0.9262 | 0.9545 | 0.9402 | 0.9787 |
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- | 0.0889 | 1.8954 | 1160 | 0.0788 | 0.9221 | 0.9531 | 0.9373 | 0.9772 |
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- | 0.0782 | 1.9281 | 1180 | 0.0828 | 0.9182 | 0.9561 | 0.9367 | 0.9766 |
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- | 0.0865 | 1.9608 | 1200 | 0.0825 | 0.9235 | 0.9545 | 0.9387 | 0.9773 |
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- | 0.085 | 1.9935 | 1220 | 0.0756 | 0.9246 | 0.9639 | 0.9438 | 0.9797 |
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- | 0.0592 | 2.0261 | 1240 | 0.0767 | 0.9335 | 0.9510 | 0.9422 | 0.9788 |
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- | 0.0662 | 2.0588 | 1260 | 0.0757 | 0.9310 | 0.9515 | 0.9412 | 0.9793 |
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- | 0.0604 | 2.0915 | 1280 | 0.0765 | 0.9265 | 0.9580 | 0.9420 | 0.9792 |
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- | 0.0435 | 2.1242 | 1300 | 0.0739 | 0.9193 | 0.9552 | 0.9369 | 0.9791 |
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- | 0.0558 | 2.1569 | 1320 | 0.0725 | 0.9367 | 0.9464 | 0.9415 | 0.9801 |
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- | 0.0919 | 2.1895 | 1340 | 0.0790 | 0.9208 | 0.9557 | 0.9379 | 0.9774 |
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- | 0.054 | 2.2222 | 1360 | 0.0784 | 0.9242 | 0.9574 | 0.9405 | 0.9780 |
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- | 0.0595 | 2.2549 | 1380 | 0.0757 | 0.9355 | 0.9570 | 0.9462 | 0.9805 |
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- | 0.0688 | 2.2876 | 1400 | 0.0802 | 0.9214 | 0.9536 | 0.9372 | 0.9772 |
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- | 0.0579 | 2.3203 | 1420 | 0.0788 | 0.9296 | 0.9527 | 0.9410 | 0.9777 |
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- | 0.0536 | 2.3529 | 1440 | 0.0717 | 0.9407 | 0.9510 | 0.9458 | 0.9807 |
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- | 0.0574 | 2.3856 | 1460 | 0.0781 | 0.9309 | 0.9628 | 0.9466 | 0.9800 |
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- | 0.0638 | 2.4183 | 1480 | 0.0751 | 0.9392 | 0.9501 | 0.9446 | 0.9795 |
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- | 0.0865 | 2.4510 | 1500 | 0.0904 | 0.8958 | 0.9484 | 0.9213 | 0.9730 |
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- | 0.0829 | 2.4837 | 1520 | 0.0716 | 0.9394 | 0.9592 | 0.9492 | 0.9813 |
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- | 0.054 | 2.5163 | 1540 | 0.0747 | 0.9397 | 0.9555 | 0.9475 | 0.9808 |
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- | 0.0726 | 2.5490 | 1560 | 0.0754 | 0.9319 | 0.9645 | 0.9479 | 0.9812 |
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- | 0.0702 | 2.5817 | 1580 | 0.0720 | 0.9313 | 0.9598 | 0.9453 | 0.9805 |
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- | 0.0662 | 2.6144 | 1600 | 0.0677 | 0.9392 | 0.9566 | 0.9478 | 0.9814 |
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- | 0.054 | 2.6471 | 1620 | 0.0740 | 0.9367 | 0.9539 | 0.9452 | 0.9798 |
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- | 0.0567 | 2.6797 | 1640 | 0.0717 | 0.9391 | 0.9536 | 0.9463 | 0.9804 |
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- | 0.0623 | 2.7124 | 1660 | 0.0757 | 0.9333 | 0.9587 | 0.9459 | 0.9801 |
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- | 0.0671 | 2.7451 | 1680 | 0.0729 | 0.9270 | 0.9521 | 0.9394 | 0.9797 |
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- | 0.0477 | 2.7778 | 1700 | 0.0783 | 0.9223 | 0.9587 | 0.9401 | 0.9786 |
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- | 0.0675 | 2.8105 | 1720 | 0.0688 | 0.9357 | 0.9616 | 0.9485 | 0.9817 |
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- | 0.0719 | 2.8431 | 1740 | 0.0707 | 0.9348 | 0.9607 | 0.9476 | 0.9807 |
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- | 0.0508 | 2.8758 | 1760 | 0.0724 | 0.9284 | 0.9567 | 0.9423 | 0.9794 |
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- | 0.047 | 2.9085 | 1780 | 0.0746 | 0.9324 | 0.9543 | 0.9432 | 0.9790 |
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- | 0.056 | 2.9412 | 1800 | 0.0700 | 0.9348 | 0.9577 | 0.9461 | 0.9806 |
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- | 0.0597 | 2.9739 | 1820 | 0.0699 | 0.9362 | 0.9612 | 0.9486 | 0.9814 |
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- | 0.0449 | 3.0065 | 1840 | 0.0692 | 0.9377 | 0.9633 | 0.9503 | 0.9824 |
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- | 0.0487 | 3.0392 | 1860 | 0.0748 | 0.9234 | 0.9518 | 0.9373 | 0.9791 |
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- | 0.0384 | 3.0719 | 1880 | 0.0689 | 0.9335 | 0.9591 | 0.9461 | 0.9815 |
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- | 0.0453 | 3.1046 | 1900 | 0.0695 | 0.9370 | 0.9583 | 0.9476 | 0.9813 |
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- | 0.0505 | 3.1373 | 1920 | 0.0782 | 0.9277 | 0.9586 | 0.9429 | 0.9790 |
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- | 0.0439 | 3.1699 | 1940 | 0.0707 | 0.9349 | 0.9619 | 0.9482 | 0.9816 |
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- | 0.0365 | 3.2026 | 1960 | 0.0692 | 0.9354 | 0.9563 | 0.9457 | 0.9815 |
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- | 0.0436 | 3.2353 | 1980 | 0.0733 | 0.9270 | 0.9587 | 0.9426 | 0.9798 |
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- | 0.0447 | 3.2680 | 2000 | 0.0731 | 0.9352 | 0.9589 | 0.9469 | 0.9807 |
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- | 0.0398 | 3.3007 | 2020 | 0.0678 | 0.9432 | 0.9605 | 0.9518 | 0.9829 |
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- | 0.0562 | 3.3333 | 2040 | 0.0720 | 0.9298 | 0.9522 | 0.9409 | 0.9797 |
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- | 0.041 | 3.3660 | 2060 | 0.0653 | 0.9462 | 0.9591 | 0.9526 | 0.9832 |
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- | 0.0463 | 3.3987 | 2080 | 0.0712 | 0.9331 | 0.9656 | 0.9491 | 0.9819 |
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- | 0.0442 | 3.4314 | 2100 | 0.0678 | 0.9407 | 0.9652 | 0.9528 | 0.9833 |
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- | 0.0512 | 3.4641 | 2120 | 0.0707 | 0.9313 | 0.9572 | 0.9440 | 0.9805 |
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- | 0.0442 | 3.4967 | 2140 | 0.0714 | 0.9314 | 0.9585 | 0.9447 | 0.9803 |
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- | 0.0432 | 3.5294 | 2160 | 0.0683 | 0.9351 | 0.9552 | 0.9451 | 0.9813 |
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- | 0.0399 | 3.5621 | 2180 | 0.0694 | 0.9334 | 0.9472 | 0.9402 | 0.9804 |
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- | 0.0426 | 3.5948 | 2200 | 0.0711 | 0.9307 | 0.9534 | 0.9419 | 0.9801 |
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- | 0.0337 | 3.6275 | 2220 | 0.0659 | 0.9418 | 0.9594 | 0.9506 | 0.9827 |
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- | 0.0293 | 3.6601 | 2240 | 0.0658 | 0.9441 | 0.9568 | 0.9504 | 0.9826 |
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- | 0.0473 | 3.6928 | 2260 | 0.0686 | 0.9377 | 0.9555 | 0.9465 | 0.9814 |
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- | 0.0406 | 3.7255 | 2280 | 0.0674 | 0.9392 | 0.9561 | 0.9476 | 0.9821 |
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- | 0.0495 | 3.7582 | 2300 | 0.0692 | 0.9369 | 0.9597 | 0.9481 | 0.9815 |
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- | 0.0366 | 3.7908 | 2320 | 0.0685 | 0.9394 | 0.9603 | 0.9497 | 0.9818 |
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- | 0.0526 | 3.8235 | 2340 | 0.0689 | 0.9380 | 0.9605 | 0.9491 | 0.9818 |
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- | 0.0444 | 3.8562 | 2360 | 0.0691 | 0.9385 | 0.9611 | 0.9497 | 0.9820 |
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- | 0.0447 | 3.8889 | 2380 | 0.0700 | 0.9386 | 0.9618 | 0.9501 | 0.9818 |
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- | 0.0498 | 3.9216 | 2400 | 0.0709 | 0.9363 | 0.9605 | 0.9482 | 0.9812 |
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- | 0.0424 | 3.9542 | 2420 | 0.0703 | 0.9371 | 0.9598 | 0.9483 | 0.9814 |
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- | 0.0518 | 3.9869 | 2440 | 0.0698 | 0.9379 | 0.9598 | 0.9487 | 0.9815 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0681
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+ - Precision: 0.9324
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+ - Recall: 0.9599
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+ - F1: 0.9460
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+ - Accuracy: 0.9808
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0004
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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  - seed: 42
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  - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - training_steps: 1224
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 2.5288 | 0.0654 | 20 | 1.2145 | 0.0084 | 0.0001 | 0.0002 | 0.7183 |
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+ | 0.9544 | 0.1307 | 40 | 0.4841 | 0.7484 | 0.6219 | 0.6793 | 0.8849 |
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+ | 0.4878 | 0.1961 | 60 | 0.2727 | 0.8256 | 0.7528 | 0.7875 | 0.9225 |
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+ | 0.3072 | 0.2614 | 80 | 0.1840 | 0.8173 | 0.8716 | 0.8436 | 0.9486 |
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+ | 0.2213 | 0.3268 | 100 | 0.1585 | 0.8248 | 0.9059 | 0.8634 | 0.9547 |
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+ | 0.2246 | 0.3922 | 120 | 0.1568 | 0.8380 | 0.9193 | 0.8768 | 0.9552 |
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+ | 0.1715 | 0.4575 | 140 | 0.1099 | 0.9058 | 0.9117 | 0.9087 | 0.9663 |
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+ | 0.1591 | 0.5229 | 160 | 0.1138 | 0.8865 | 0.9488 | 0.9166 | 0.9680 |
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+ | 0.1514 | 0.5882 | 180 | 0.0932 | 0.9002 | 0.9386 | 0.9190 | 0.9715 |
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+ | 0.1216 | 0.6536 | 200 | 0.0903 | 0.9097 | 0.9449 | 0.9270 | 0.9729 |
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+ | 0.134 | 0.7190 | 220 | 0.0949 | 0.9129 | 0.9275 | 0.9201 | 0.9715 |
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+ | 0.1329 | 0.7843 | 240 | 0.1017 | 0.8967 | 0.9422 | 0.9189 | 0.9706 |
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+ | 0.1192 | 0.8497 | 260 | 0.0929 | 0.9097 | 0.9367 | 0.9230 | 0.9723 |
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+ | 0.1266 | 0.9150 | 280 | 0.1050 | 0.8881 | 0.9356 | 0.9112 | 0.9691 |
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+ | 0.1332 | 0.9804 | 300 | 0.0963 | 0.9078 | 0.9343 | 0.9208 | 0.9716 |
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+ | 0.1218 | 1.0458 | 320 | 0.0887 | 0.9104 | 0.9416 | 0.9257 | 0.9730 |
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+ | 0.0943 | 1.1111 | 340 | 0.0904 | 0.9119 | 0.9469 | 0.9291 | 0.9733 |
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+ | 0.1033 | 1.1765 | 360 | 0.0995 | 0.9035 | 0.9470 | 0.9247 | 0.9706 |
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+ | 0.1053 | 1.2418 | 380 | 0.0829 | 0.9197 | 0.9439 | 0.9316 | 0.9766 |
81
+ | 0.1032 | 1.3072 | 400 | 0.0795 | 0.9150 | 0.9471 | 0.9308 | 0.9759 |
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+ | 0.1079 | 1.3725 | 420 | 0.0870 | 0.8990 | 0.9285 | 0.9135 | 0.9715 |
83
+ | 0.1009 | 1.4379 | 440 | 0.0801 | 0.9250 | 0.9478 | 0.9363 | 0.9771 |
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+ | 0.093 | 1.5033 | 460 | 0.0713 | 0.9341 | 0.9459 | 0.9399 | 0.9782 |
85
+ | 0.0909 | 1.5686 | 480 | 0.0762 | 0.9214 | 0.9556 | 0.9382 | 0.9774 |
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+ | 0.0853 | 1.6340 | 500 | 0.0824 | 0.9152 | 0.9483 | 0.9315 | 0.9758 |
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+ | 0.1002 | 1.6993 | 520 | 0.0933 | 0.9031 | 0.9539 | 0.9278 | 0.9737 |
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+ | 0.0917 | 1.7647 | 540 | 0.0979 | 0.8713 | 0.9204 | 0.8952 | 0.9677 |
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+ | 0.127 | 1.8301 | 560 | 0.1236 | 0.9003 | 0.9273 | 0.9136 | 0.9674 |
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+ | 0.1221 | 1.8954 | 580 | 0.1022 | 0.9089 | 0.9346 | 0.9216 | 0.9711 |
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+ | 0.1039 | 1.9608 | 600 | 0.0946 | 0.9052 | 0.9385 | 0.9215 | 0.9725 |
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+ | 0.0873 | 2.0261 | 620 | 0.0914 | 0.9060 | 0.9521 | 0.9285 | 0.9737 |
93
+ | 0.0736 | 2.0915 | 640 | 0.0765 | 0.9228 | 0.9509 | 0.9366 | 0.9776 |
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+ | 0.0584 | 2.1569 | 660 | 0.0795 | 0.9179 | 0.9423 | 0.9300 | 0.9761 |
95
+ | 0.0858 | 2.2222 | 680 | 0.0764 | 0.9229 | 0.9495 | 0.9360 | 0.9766 |
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+ | 0.0849 | 2.2876 | 700 | 0.0797 | 0.9194 | 0.9420 | 0.9305 | 0.9768 |
97
+ | 0.0626 | 2.3529 | 720 | 0.0729 | 0.9327 | 0.9527 | 0.9426 | 0.9789 |
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+ | 0.0725 | 2.4183 | 740 | 0.0747 | 0.9246 | 0.9574 | 0.9407 | 0.9781 |
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+ | 0.0914 | 2.4837 | 760 | 0.0796 | 0.9196 | 0.9579 | 0.9383 | 0.9774 |
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+ | 0.0676 | 2.5490 | 780 | 0.0762 | 0.9297 | 0.9572 | 0.9432 | 0.9793 |
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+ | 0.0724 | 2.6144 | 800 | 0.0710 | 0.9388 | 0.9533 | 0.9460 | 0.9809 |
102
+ | 0.0635 | 2.6797 | 820 | 0.0757 | 0.9303 | 0.9520 | 0.9410 | 0.9780 |
103
+ | 0.0729 | 2.7451 | 840 | 0.0724 | 0.9279 | 0.9536 | 0.9406 | 0.9793 |
104
+ | 0.061 | 2.8105 | 860 | 0.0711 | 0.9278 | 0.9522 | 0.9399 | 0.9793 |
105
+ | 0.0646 | 2.8758 | 880 | 0.0792 | 0.9207 | 0.9544 | 0.9372 | 0.9767 |
106
+ | 0.0602 | 2.9412 | 900 | 0.0721 | 0.9246 | 0.9549 | 0.9395 | 0.9785 |
107
+ | 0.0568 | 3.0065 | 920 | 0.0685 | 0.9333 | 0.9540 | 0.9435 | 0.9804 |
108
+ | 0.0518 | 3.0719 | 940 | 0.0742 | 0.9239 | 0.9574 | 0.9403 | 0.9789 |
109
+ | 0.0547 | 3.1373 | 960 | 0.0798 | 0.9209 | 0.9573 | 0.9387 | 0.9778 |
110
+ | 0.0454 | 3.2026 | 980 | 0.0697 | 0.9366 | 0.9564 | 0.9464 | 0.9810 |
111
+ | 0.0549 | 3.2680 | 1000 | 0.0753 | 0.9253 | 0.9606 | 0.9426 | 0.9785 |
112
+ | 0.0534 | 3.3333 | 1020 | 0.0690 | 0.9345 | 0.9574 | 0.9458 | 0.9808 |
113
+ | 0.0527 | 3.3987 | 1040 | 0.0681 | 0.9297 | 0.9604 | 0.9448 | 0.9801 |
114
+ | 0.057 | 3.4641 | 1060 | 0.0672 | 0.9346 | 0.9585 | 0.9464 | 0.9812 |
115
+ | 0.0482 | 3.5294 | 1080 | 0.0705 | 0.9268 | 0.9569 | 0.9416 | 0.9801 |
116
+ | 0.0482 | 3.5948 | 1100 | 0.0689 | 0.9304 | 0.9566 | 0.9433 | 0.9804 |
117
+ | 0.0412 | 3.6601 | 1120 | 0.0670 | 0.9345 | 0.9609 | 0.9475 | 0.9815 |
118
+ | 0.0565 | 3.7255 | 1140 | 0.0676 | 0.9334 | 0.9603 | 0.9467 | 0.9810 |
119
+ | 0.0509 | 3.7908 | 1160 | 0.0672 | 0.9347 | 0.9615 | 0.9479 | 0.9814 |
120
+ | 0.0566 | 3.8562 | 1180 | 0.0684 | 0.9316 | 0.9601 | 0.9457 | 0.9806 |
121
+ | 0.0602 | 3.9216 | 1200 | 0.0690 | 0.9317 | 0.9601 | 0.9457 | 0.9805 |
122
+ | 0.0585 | 3.9869 | 1220 | 0.0681 | 0.9324 | 0.9599 | 0.9460 | 0.9808 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions