---
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_fnir
  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_fnir

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: 0.0113
- Accuracy: 0.998
- Auc: 1.0
- Precision: 1.0
- Recall: 0.996
- F1: 0.998
- F1-macro: 0.998
- F1-micro: 0.998
- F1-weighted: 0.998

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:|
| 0.0666        | 0.6024 | 100  | 0.0142          | 0.997    | 1.0 | 1.0       | 0.995  | 0.997 | 0.997    | 0.997    | 0.997       |
| 0.0146        | 1.2048 | 200  | 0.0124          | 0.997    | 1.0 | 1.0       | 0.993  | 0.997 | 0.997    | 0.997    | 0.997       |
| 0.0006        | 1.8072 | 300  | 0.0123          | 0.998    | 1.0 | 1.0       | 0.996  | 0.998 | 0.998    | 0.998    | 0.998       |
| 0.0045        | 2.4096 | 400  | 0.0134          | 0.997    | 1.0 | 0.999     | 0.996  | 0.997 | 0.997    | 0.997    | 0.997       |
| 0.0028        | 3.0120 | 500  | 0.0116          | 0.998    | 1.0 | 1.0       | 0.996  | 0.998 | 0.998    | 0.998    | 0.998       |
| 0.0025        | 3.6145 | 600  | 0.0131          | 0.998    | 1.0 | 1.0       | 0.996  | 0.998 | 0.998    | 0.998    | 0.998       |
| 0.003         | 4.2169 | 700  | 0.0104          | 0.998    | 1.0 | 1.0       | 0.996  | 0.998 | 0.998    | 0.998    | 0.998       |
| 0.0002        | 4.8193 | 800  | 0.0113          | 0.998    | 1.0 | 1.0       | 0.996  | 0.998 | 0.998    | 0.998    | 0.998       |


### Framework versions

- Transformers 4.53.0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2