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bert-medical-ner

Token Classification
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PyTorch
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distilbert
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update model card README.md

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3023
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- - Precision: 0.6627
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- - Recall: 0.6985
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- - F1: 0.6802
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- - Accuracy: 0.7491
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  ## Model description
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@@ -54,56 +54,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 71 | 1.8664 | 0.3387 | 0.4366 | 0.3815 | 0.5491 |
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- | No log | 2.0 | 142 | 1.3020 | 0.4581 | 0.5572 | 0.5028 | 0.6561 |
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- | No log | 3.0 | 213 | 1.1061 | 0.5318 | 0.6091 | 0.5678 | 0.6921 |
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- | No log | 4.0 | 284 | 0.9755 | 0.6177 | 0.6383 | 0.6278 | 0.7193 |
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- | No log | 5.0 | 355 | 0.9530 | 0.6071 | 0.6362 | 0.6213 | 0.7272 |
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- | No log | 6.0 | 426 | 0.8876 | 0.6456 | 0.6590 | 0.6523 | 0.7351 |
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- | No log | 7.0 | 497 | 0.8754 | 0.6674 | 0.6757 | 0.6715 | 0.7386 |
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- | 1.158 | 8.0 | 568 | 0.8472 | 0.6782 | 0.6923 | 0.6852 | 0.7491 |
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- | 1.158 | 9.0 | 639 | 0.8816 | 0.6573 | 0.6819 | 0.6694 | 0.7368 |
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- | 1.158 | 10.0 | 710 | 0.9035 | 0.6260 | 0.6299 | 0.6280 | 0.7184 |
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- | 1.158 | 11.0 | 781 | 0.9156 | 0.6573 | 0.6819 | 0.6694 | 0.7377 |
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- | 1.158 | 12.0 | 852 | 0.8764 | 0.6536 | 0.6944 | 0.6734 | 0.7456 |
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- | 1.158 | 13.0 | 923 | 0.9079 | 0.6673 | 0.6881 | 0.6776 | 0.7404 |
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- | 1.158 | 14.0 | 994 | 0.9278 | 0.6525 | 0.6715 | 0.6619 | 0.7351 |
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- | 0.4312 | 15.0 | 1065 | 0.9387 | 0.6755 | 0.6923 | 0.6838 | 0.7465 |
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- | 0.4312 | 16.0 | 1136 | 0.9396 | 0.6595 | 0.7006 | 0.6794 | 0.7482 |
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- | 0.4312 | 17.0 | 1207 | 0.9672 | 0.648 | 0.6736 | 0.6606 | 0.7351 |
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- | 0.4312 | 18.0 | 1278 | 0.9890 | 0.6719 | 0.7110 | 0.6909 | 0.7509 |
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- | 0.4312 | 19.0 | 1349 | 1.0124 | 0.6344 | 0.6819 | 0.6573 | 0.7368 |
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- | 0.4312 | 20.0 | 1420 | 1.0107 | 0.6564 | 0.7069 | 0.6807 | 0.7526 |
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- | 0.4312 | 21.0 | 1491 | 1.0036 | 0.6765 | 0.7131 | 0.6943 | 0.7632 |
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- | 0.2196 | 22.0 | 1562 | 1.0244 | 0.6744 | 0.7235 | 0.6981 | 0.7561 |
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- | 0.2196 | 23.0 | 1633 | 1.0668 | 0.6602 | 0.7027 | 0.6808 | 0.7430 |
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- | 0.2196 | 24.0 | 1704 | 1.1040 | 0.6667 | 0.7193 | 0.6920 | 0.7526 |
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- | 0.2196 | 25.0 | 1775 | 1.0959 | 0.6699 | 0.7173 | 0.6928 | 0.7553 |
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- | 0.2196 | 26.0 | 1846 | 1.0721 | 0.6765 | 0.7173 | 0.6963 | 0.7544 |
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- | 0.2196 | 27.0 | 1917 | 1.1114 | 0.6628 | 0.7069 | 0.6841 | 0.7553 |
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- | 0.2196 | 28.0 | 1988 | 1.1225 | 0.6429 | 0.6923 | 0.6667 | 0.7421 |
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- | 0.1279 | 29.0 | 2059 | 1.1149 | 0.6481 | 0.7006 | 0.6733 | 0.7588 |
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- | 0.1279 | 30.0 | 2130 | 1.1545 | 0.6660 | 0.7048 | 0.6848 | 0.7544 |
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- | 0.1279 | 31.0 | 2201 | 1.1645 | 0.6641 | 0.7152 | 0.6887 | 0.7535 |
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- | 0.1279 | 32.0 | 2272 | 1.2004 | 0.6523 | 0.6944 | 0.6727 | 0.7386 |
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- | 0.1279 | 33.0 | 2343 | 1.2030 | 0.6419 | 0.6819 | 0.6613 | 0.7404 |
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- | 0.1279 | 34.0 | 2414 | 1.2434 | 0.6726 | 0.7048 | 0.6883 | 0.7482 |
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- | 0.1279 | 35.0 | 2485 | 1.2795 | 0.6548 | 0.6902 | 0.6721 | 0.7412 |
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- | 0.0843 | 36.0 | 2556 | 1.2499 | 0.6772 | 0.7152 | 0.6957 | 0.7544 |
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- | 0.0843 | 37.0 | 2627 | 1.2545 | 0.6745 | 0.7152 | 0.6942 | 0.7535 |
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- | 0.0843 | 38.0 | 2698 | 1.2286 | 0.6680 | 0.6985 | 0.6829 | 0.75 |
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- | 0.0843 | 39.0 | 2769 | 1.2943 | 0.6601 | 0.6985 | 0.6788 | 0.7518 |
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- | 0.0843 | 40.0 | 2840 | 1.2713 | 0.6640 | 0.7027 | 0.6828 | 0.7535 |
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- | 0.0843 | 41.0 | 2911 | 1.2828 | 0.6510 | 0.6902 | 0.6700 | 0.7465 |
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- | 0.0843 | 42.0 | 2982 | 1.2830 | 0.6621 | 0.7048 | 0.6828 | 0.7509 |
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- | 0.0619 | 43.0 | 3053 | 1.2942 | 0.6621 | 0.6965 | 0.6788 | 0.75 |
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- | 0.0619 | 44.0 | 3124 | 1.2912 | 0.6752 | 0.7089 | 0.6917 | 0.7544 |
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- | 0.0619 | 45.0 | 3195 | 1.2631 | 0.6680 | 0.7069 | 0.6869 | 0.7579 |
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- | 0.0619 | 46.0 | 3266 | 1.2948 | 0.6647 | 0.7006 | 0.6822 | 0.7535 |
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- | 0.0619 | 47.0 | 3337 | 1.2829 | 0.6739 | 0.7131 | 0.6929 | 0.7570 |
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- | 0.0619 | 48.0 | 3408 | 1.2943 | 0.6602 | 0.7027 | 0.6808 | 0.75 |
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- | 0.0619 | 49.0 | 3479 | 1.2995 | 0.6562 | 0.6944 | 0.6747 | 0.7465 |
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- | 0.0514 | 50.0 | 3550 | 1.3023 | 0.6627 | 0.6985 | 0.6802 | 0.7491 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1905
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+ - Precision: 0.6552
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+ - Recall: 0.6965
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+ - F1: 0.6752
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+ - Accuracy: 0.7449
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 71 | 1.8255 | 0.3427 | 0.4460 | 0.3876 | 0.5555 |
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+ | No log | 2.0 | 142 | 1.3139 | 0.4722 | 0.5703 | 0.5166 | 0.6442 |
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+ | No log | 3.0 | 213 | 1.1147 | 0.5258 | 0.6029 | 0.5617 | 0.6886 |
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+ | No log | 4.0 | 284 | 0.9873 | 0.5785 | 0.6151 | 0.5962 | 0.7048 |
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+ | No log | 5.0 | 355 | 0.9282 | 0.6314 | 0.6558 | 0.6434 | 0.7312 |
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+ | No log | 6.0 | 426 | 0.8760 | 0.642 | 0.6538 | 0.6478 | 0.7329 |
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+ | No log | 7.0 | 497 | 0.8501 | 0.6608 | 0.6904 | 0.6753 | 0.7466 |
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+ | 1.1706 | 8.0 | 568 | 0.8313 | 0.6791 | 0.7067 | 0.6926 | 0.7483 |
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+ | 1.1706 | 9.0 | 639 | 0.8002 | 0.6616 | 0.7047 | 0.6824 | 0.7449 |
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+ | 1.1706 | 10.0 | 710 | 0.8280 | 0.6640 | 0.6721 | 0.6680 | 0.7363 |
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+ | 1.1706 | 11.0 | 781 | 0.8248 | 0.6594 | 0.6823 | 0.6707 | 0.7457 |
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+ | 1.1706 | 12.0 | 852 | 0.7988 | 0.6610 | 0.7189 | 0.6888 | 0.7654 |
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+ | 1.1706 | 13.0 | 923 | 0.8593 | 0.6587 | 0.6762 | 0.6673 | 0.7423 |
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+ | 1.1706 | 14.0 | 994 | 0.8204 | 0.6719 | 0.6965 | 0.6840 | 0.7534 |
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+ | 0.4317 | 15.0 | 1065 | 0.8478 | 0.6770 | 0.7128 | 0.6944 | 0.7526 |
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+ | 0.4317 | 16.0 | 1136 | 0.8855 | 0.6610 | 0.7149 | 0.6869 | 0.7730 |
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+ | 0.4317 | 17.0 | 1207 | 0.9091 | 0.6751 | 0.7067 | 0.6905 | 0.7560 |
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+ | 0.4317 | 18.0 | 1278 | 0.9201 | 0.6555 | 0.7169 | 0.6848 | 0.7568 |
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+ | 0.4317 | 19.0 | 1349 | 0.9840 | 0.6623 | 0.7189 | 0.6895 | 0.7483 |
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+ | 0.4317 | 20.0 | 1420 | 0.9817 | 0.6833 | 0.7251 | 0.7036 | 0.7543 |
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+ | 0.4317 | 21.0 | 1491 | 0.9958 | 0.6583 | 0.6945 | 0.6759 | 0.7509 |
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+ | 0.2121 | 22.0 | 1562 | 0.9340 | 0.6647 | 0.7026 | 0.6832 | 0.7722 |
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+ | 0.2121 | 23.0 | 1633 | 0.9906 | 0.6622 | 0.7108 | 0.6857 | 0.7619 |
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+ | 0.2121 | 24.0 | 1704 | 1.0099 | 0.6692 | 0.7088 | 0.6884 | 0.7526 |
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+ | 0.2121 | 25.0 | 1775 | 1.0627 | 0.6673 | 0.7189 | 0.6922 | 0.7662 |
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+ | 0.2121 | 26.0 | 1846 | 1.0744 | 0.6584 | 0.7067 | 0.6817 | 0.7637 |
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+ | 0.2121 | 27.0 | 1917 | 1.1328 | 0.6569 | 0.6864 | 0.6713 | 0.7389 |
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+ | 0.2121 | 28.0 | 1988 | 1.0799 | 0.6641 | 0.7128 | 0.6876 | 0.7577 |
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+ | 0.1201 | 29.0 | 2059 | 1.1156 | 0.6628 | 0.7047 | 0.6831 | 0.7568 |
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+ | 0.1201 | 30.0 | 2130 | 1.0839 | 0.6628 | 0.6965 | 0.6792 | 0.75 |
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+ | 0.1201 | 31.0 | 2201 | 1.1511 | 0.6526 | 0.6925 | 0.6719 | 0.7389 |
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+ | 0.1201 | 32.0 | 2272 | 1.1140 | 0.6737 | 0.7149 | 0.6937 | 0.7543 |
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+ | 0.1201 | 33.0 | 2343 | 1.1094 | 0.6609 | 0.6986 | 0.6792 | 0.7466 |
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+ | 0.1201 | 34.0 | 2414 | 1.1332 | 0.6755 | 0.7251 | 0.6994 | 0.7534 |
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+ | 0.1201 | 35.0 | 2485 | 1.1322 | 0.6841 | 0.7189 | 0.7011 | 0.7551 |
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+ | 0.0776 | 36.0 | 2556 | 1.1603 | 0.6711 | 0.7189 | 0.6942 | 0.7551 |
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+ | 0.0776 | 37.0 | 2627 | 1.1460 | 0.6504 | 0.7047 | 0.6764 | 0.7543 |
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+ | 0.0776 | 38.0 | 2698 | 1.1387 | 0.6584 | 0.7067 | 0.6817 | 0.7577 |
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+ | 0.0776 | 39.0 | 2769 | 1.1438 | 0.6641 | 0.7088 | 0.6857 | 0.7534 |
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+ | 0.0776 | 40.0 | 2840 | 1.1791 | 0.6660 | 0.7149 | 0.6896 | 0.7577 |
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+ | 0.0776 | 41.0 | 2911 | 1.1701 | 0.6641 | 0.7088 | 0.6857 | 0.75 |
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+ | 0.0776 | 42.0 | 2982 | 1.1889 | 0.6615 | 0.6965 | 0.6786 | 0.7457 |
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+ | 0.0571 | 43.0 | 3053 | 1.1810 | 0.6533 | 0.6945 | 0.6732 | 0.7449 |
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+ | 0.0571 | 44.0 | 3124 | 1.1944 | 0.6577 | 0.6965 | 0.6766 | 0.7440 |
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+ | 0.0571 | 45.0 | 3195 | 1.2032 | 0.6564 | 0.6925 | 0.6739 | 0.7432 |
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+ | 0.0571 | 46.0 | 3266 | 1.2092 | 0.6609 | 0.6945 | 0.6773 | 0.7449 |
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+ | 0.0571 | 47.0 | 3337 | 1.1864 | 0.6622 | 0.6986 | 0.6799 | 0.7466 |
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+ | 0.0571 | 48.0 | 3408 | 1.1972 | 0.6538 | 0.6925 | 0.6726 | 0.7449 |
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+ | 0.0571 | 49.0 | 3479 | 1.1899 | 0.6545 | 0.6945 | 0.6739 | 0.7449 |
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+ | 0.0467 | 50.0 | 3550 | 1.1905 | 0.6552 | 0.6965 | 0.6752 | 0.7449 |
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  ### Framework versions