metadata
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-T17-21-20
results: []
biobert-v1.1-finetuned-medmcqa-2024-11-25-T17-21-20
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2111
- Accuracy: 0.5742
- F1: 0.5753
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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.7483 | 0.9939 | 142 | 0.9367 | 0.5613 | 0.5628 |
| 0.6026 | 1.9921 | 284 | 0.9227 | 0.5685 | 0.5693 |
| 0.3491 | 2.9904 | 426 | 1.2111 | 0.5742 | 0.5753 |
| 0.2135 | 3.9956 | 569 | 1.5260 | 0.5697 | 0.5704 |
| 0.114 | 4.9869 | 710 | 1.9557 | 0.5666 | 0.5672 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3