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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-medical-ner |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-medical-ner |
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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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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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 |
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- num_epochs: 50 |
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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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| 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 |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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