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biobert-base-case-ner

Token Classification
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bert
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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.6530483972344437
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  - name: Recall
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  type: recall
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- value: 0.7162224264705882
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  - name: F1
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  type: f1
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- value: 0.6831780821917808
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  - name: Accuracy
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  type: accuracy
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- value: 0.9547333889783954
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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
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the conll2002 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1655
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- - Precision: 0.6530
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- - Recall: 0.7162
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- - F1: 0.6832
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- - Accuracy: 0.9547
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  ## Model description
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@@ -72,19 +72,27 @@ 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: 2
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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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- | 0.1257 | 1.0 | 1041 | 0.1724 | 0.6232 | 0.6788 | 0.6498 | 0.9510 |
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- | 0.0846 | 2.0 | 2082 | 0.1655 | 0.6530 | 0.7162 | 0.6832 | 0.9547 |
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.41.1
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  - Pytorch 2.3.0+cu121
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- - Datasets 2.19.1
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  - Tokenizers 0.19.1
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.7494539100043687
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  - name: Recall
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  type: recall
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+ value: 0.7883731617647058
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  - name: F1
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  type: f1
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+ value: 0.7684210526315789
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9629927984937011
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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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  This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the conll2002 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2531
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+ - Precision: 0.7495
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+ - Recall: 0.7884
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+ - F1: 0.7684
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+ - Accuracy: 0.9630
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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: 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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+ | 0.1214 | 1.0 | 1041 | 0.1681 | 0.6611 | 0.6997 | 0.6798 | 0.9523 |
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+ | 0.0814 | 2.0 | 2082 | 0.1652 | 0.6692 | 0.7270 | 0.6969 | 0.9540 |
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+ | 0.0531 | 3.0 | 3123 | 0.1628 | 0.7291 | 0.7682 | 0.7481 | 0.9624 |
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+ | 0.0357 | 4.0 | 4164 | 0.1799 | 0.7427 | 0.7721 | 0.7571 | 0.9620 |
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+ | 0.0277 | 5.0 | 5205 | 0.1963 | 0.7530 | 0.7824 | 0.7674 | 0.9627 |
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+ | 0.0168 | 6.0 | 6246 | 0.2115 | 0.7333 | 0.7771 | 0.7546 | 0.9615 |
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+ | 0.0136 | 7.0 | 7287 | 0.2311 | 0.7376 | 0.7769 | 0.7567 | 0.9613 |
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+ | 0.0106 | 8.0 | 8328 | 0.2450 | 0.7552 | 0.7861 | 0.7703 | 0.9626 |
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+ | 0.0062 | 9.0 | 9369 | 0.2572 | 0.7589 | 0.7877 | 0.7730 | 0.9622 |
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+ | 0.0061 | 10.0 | 10410 | 0.2531 | 0.7495 | 0.7884 | 0.7684 | 0.9630 |
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  ### Framework versions
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  - Transformers 4.41.1
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  - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.2
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  - Tokenizers 0.19.1