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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: 0.3752
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- - Precision: 0.7403
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- - Recall: 0.6800
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- - F1: 0.7089
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- - Accuracy: 0.9401
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  ## Model description
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@@ -48,22 +48,27 @@ 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: 10
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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 | 0.3364 | 0.6802 | 0.6433 | 0.6612 | 0.9325 |
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- | No log | 2.0 | 126 | 0.3434 | 0.6759 | 0.6789 | 0.6774 | 0.9331 |
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- | No log | 3.0 | 189 | 0.3381 | 0.7297 | 0.6851 | 0.7067 | 0.9389 |
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- | No log | 4.0 | 252 | 0.3470 | 0.6788 | 0.6942 | 0.6864 | 0.9333 |
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- | No log | 5.0 | 315 | 0.3668 | 0.7392 | 0.6761 | 0.7062 | 0.9380 |
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- | No log | 6.0 | 378 | 0.3722 | 0.7565 | 0.6710 | 0.7112 | 0.9416 |
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- | No log | 7.0 | 441 | 0.3669 | 0.7253 | 0.6806 | 0.7022 | 0.9386 |
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- | 0.0633 | 8.0 | 504 | 0.3673 | 0.7250 | 0.6914 | 0.7078 | 0.9404 |
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- | 0.0633 | 9.0 | 567 | 0.3789 | 0.7456 | 0.6744 | 0.7082 | 0.9405 |
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- | 0.0633 | 10.0 | 630 | 0.3752 | 0.7403 | 0.6800 | 0.7089 | 0.9401 |
 
 
 
 
 
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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: 0.4386
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+ - Precision: 0.7582
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+ - Recall: 0.7089
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+ - F1: 0.7327
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+ - Accuracy: 0.9434
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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: 15
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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 | 0.4577 | 0.8113 | 0.6343 | 0.7119 | 0.9397 |
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+ | No log | 2.0 | 126 | 0.4098 | 0.7941 | 0.6563 | 0.7187 | 0.9419 |
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+ | No log | 3.0 | 189 | 0.3993 | 0.7369 | 0.6744 | 0.7043 | 0.9389 |
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+ | No log | 4.0 | 252 | 0.4033 | 0.7312 | 0.7089 | 0.7199 | 0.9418 |
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+ | No log | 5.0 | 315 | 0.4329 | 0.7509 | 0.6919 | 0.7202 | 0.9416 |
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+ | No log | 6.0 | 378 | 0.4343 | 0.7545 | 0.6829 | 0.7169 | 0.9420 |
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+ | No log | 7.0 | 441 | 0.4348 | 0.7168 | 0.7140 | 0.7154 | 0.9402 |
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+ | 0.0142 | 8.0 | 504 | 0.4362 | 0.7285 | 0.7055 | 0.7168 | 0.9399 |
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+ | 0.0142 | 9.0 | 567 | 0.4420 | 0.7573 | 0.7072 | 0.7314 | 0.9436 |
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+ | 0.0142 | 10.0 | 630 | 0.4371 | 0.7452 | 0.7027 | 0.7233 | 0.9423 |
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+ | 0.0142 | 11.0 | 693 | 0.4400 | 0.7648 | 0.6947 | 0.7281 | 0.9429 |
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+ | 0.0142 | 12.0 | 756 | 0.4346 | 0.7556 | 0.7027 | 0.7282 | 0.9422 |
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+ | 0.0142 | 13.0 | 819 | 0.4382 | 0.7504 | 0.7089 | 0.7291 | 0.9428 |
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+ | 0.0142 | 14.0 | 882 | 0.4368 | 0.7536 | 0.7123 | 0.7323 | 0.9434 |
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+ | 0.0142 | 15.0 | 945 | 0.4386 | 0.7582 | 0.7089 | 0.7327 | 0.9434 |
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  ### Framework versions