| | --- |
| | library_name: transformers |
| | base_model: dmis-lab/biobert-base-cased-v1.2 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: BioBert_Medhhml |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # BioBert_Medhhml |
| | |
| | This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1382 |
| | - Accuracy: 0.776 |
| | - Auc: 0.896 |
| | - Precision: 0.867 |
| | - Recall: 0.632 |
| | - F1: 0.731 |
| | - F1-macro: 0.769 |
| | - F1-micro: 0.776 |
| | - F1-weighted: 0.771 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.0001 |
| | - train_batch_size: 4 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 32 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc | Precision | Recall | F1 | F1-macro | F1-micro | F1-weighted | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:| |
| | | 0.3638 | 1.0 | 260 | 0.4250 | 0.78 | 0.908 | 0.913 | 0.603 | 0.726 | 0.771 | 0.78 | 0.773 | |
| | | 0.2463 | 2.0 | 520 | 1.7793 | 0.645 | 0.652 | 0.811 | 0.347 | 0.486 | 0.607 | 0.645 | 0.611 | |
| | | 0.1798 | 3.0 | 780 | 0.5889 | 0.768 | 0.926 | 0.883 | 0.6 | 0.715 | 0.76 | 0.768 | 0.761 | |
| | | 0.1203 | 4.0 | 1040 | 0.6651 | 0.824 | 0.935 | 0.895 | 0.72 | 0.798 | 0.821 | 0.824 | 0.821 | |
| | | 0.0785 | 5.0 | 1300 | 1.1382 | 0.776 | 0.896 | 0.867 | 0.632 | 0.731 | 0.769 | 0.776 | 0.771 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.53.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 2.14.4 |
| | - Tokenizers 0.21.2 |
| |
|
