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bert-medical-ner

Token Classification
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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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1200
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- - Precision: 0.6085
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- - Recall: 0.6398
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- - F1: 0.6237
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- - Accuracy: 0.7651
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  ## Model description
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@@ -48,17 +48,32 @@ The following hyperparameters were used during training:
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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: 5
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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 | 63 | 1.0596 | 0.6022 | 0.6300 | 0.6158 | 0.7625 |
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- | No log | 2.0 | 126 | 1.0834 | 0.5976 | 0.6414 | 0.6187 | 0.7631 |
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- | No log | 3.0 | 189 | 1.1044 | 0.6044 | 0.6360 | 0.6198 | 0.7628 |
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- | No log | 4.0 | 252 | 1.1139 | 0.6041 | 0.6364 | 0.6199 | 0.7628 |
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- | No log | 5.0 | 315 | 1.1200 | 0.6085 | 0.6398 | 0.6237 | 0.7651 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0001
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+ - Precision: 0.6207
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+ - Recall: 0.6501
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+ - F1: 0.6351
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+ - Accuracy: 0.7695
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  ## Model description
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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: 20
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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 | 63 | 1.5798 | 0.3498 | 0.3921 | 0.3697 | 0.6168 |
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+ | No log | 2.0 | 126 | 1.1942 | 0.5020 | 0.5286 | 0.5150 | 0.7028 |
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+ | No log | 3.0 | 189 | 1.0593 | 0.5345 | 0.5826 | 0.5575 | 0.7280 |
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+ | No log | 4.0 | 252 | 0.9799 | 0.5722 | 0.6065 | 0.5889 | 0.7451 |
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+ | No log | 5.0 | 315 | 0.9394 | 0.5905 | 0.6187 | 0.6043 | 0.7534 |
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+ | No log | 6.0 | 378 | 0.9171 | 0.5995 | 0.6262 | 0.6126 | 0.7576 |
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+ | No log | 7.0 | 441 | 0.9068 | 0.6071 | 0.6324 | 0.6195 | 0.7623 |
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+ | 1.0968 | 8.0 | 504 | 0.9076 | 0.6171 | 0.6323 | 0.6246 | 0.7638 |
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+ | 1.0968 | 9.0 | 567 | 0.9280 | 0.6095 | 0.6361 | 0.6225 | 0.7637 |
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+ | 1.0968 | 10.0 | 630 | 0.9231 | 0.6117 | 0.6414 | 0.6262 | 0.7670 |
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+ | 1.0968 | 11.0 | 693 | 0.9322 | 0.6183 | 0.6460 | 0.6319 | 0.7685 |
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+ | 1.0968 | 12.0 | 756 | 0.9529 | 0.6200 | 0.6503 | 0.6347 | 0.7689 |
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+ | 1.0968 | 13.0 | 819 | 0.9550 | 0.6148 | 0.6451 | 0.6296 | 0.7672 |
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+ | 1.0968 | 14.0 | 882 | 0.9736 | 0.6227 | 0.6466 | 0.6344 | 0.7688 |
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+ | 1.0968 | 15.0 | 945 | 0.9791 | 0.6206 | 0.6460 | 0.6330 | 0.7679 |
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+ | 0.4223 | 16.0 | 1008 | 0.9854 | 0.6194 | 0.6490 | 0.6339 | 0.7699 |
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+ | 0.4223 | 17.0 | 1071 | 0.9870 | 0.6185 | 0.6494 | 0.6336 | 0.7692 |
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+ | 0.4223 | 18.0 | 1134 | 0.9957 | 0.6208 | 0.6498 | 0.6350 | 0.7702 |
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+ | 0.4223 | 19.0 | 1197 | 0.9994 | 0.6189 | 0.6510 | 0.6345 | 0.7693 |
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+ | 0.4223 | 20.0 | 1260 | 1.0001 | 0.6207 | 0.6501 | 0.6351 | 0.7695 |
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  ### Framework versions