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---
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-2024-11-25-T17-12-23
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-v1.1-finetuned-medmcqa-2024-11-25-T17-12-23
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: 0.8219
- Accuracy: 0.7143
- F1: 0.7228
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.3834 | 0.9739 | 14 | 1.1666 | 0.5714 | 0.5553 |
| 1.0717 | 1.9478 | 28 | 0.9962 | 0.5952 | 0.5891 |
| 0.6143 | 2.9913 | 43 | 0.8219 | 0.7143 | 0.7228 |
| 0.4801 | 3.9652 | 57 | 0.8748 | 0.7143 | 0.7138 |
| 0.2112 | 4.9391 | 71 | 1.1275 | 0.6905 | 0.6878 |
| 0.1627 | 5.9826 | 86 | 1.2672 | 0.6905 | 0.6839 |
| 0.118 | 6.9565 | 100 | 1.4471 | 0.6429 | 0.6357 |
| 0.088 | 8.0 | 115 | 1.4548 | 0.7143 | 0.7149 |
| 0.0674 | 8.9739 | 129 | 1.4981 | 0.6905 | 0.6858 |
| 0.0715 | 9.7391 | 140 | 1.4867 | 0.7143 | 0.7126 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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