---
library_name: transformers
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: biobert-v1.1-finetuned-medmcqa-5pct-2024-12-03-T09-43-29
  results: []
---

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# biobert-v1.1-finetuned-medmcqa-5pct-2024-12-03-T09-43-29

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2074
- Accuracy: 0.5532
- F1: 0.5543

## 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: 0.000159
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.7328        | 0.9974 | 285  | 0.9662          | 0.5398   | 0.5408 |
| 0.5829        | 1.9987 | 571  | 0.9861          | 0.5520   | 0.5529 |
| 0.2838        | 2.9930 | 855  | 1.2074          | 0.5532   | 0.5543 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3