---
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