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--- |
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library_name: transformers |
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base_model: dmis-lab/biobert-v1.1 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: biobert-v1.1-finetuned-medmcqa-2024-11-25-T17-12-23 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# biobert-v1.1-finetuned-medmcqa-2024-11-25-T17-12-23 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8219 |
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- Accuracy: 0.7143 |
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- F1: 0.7228 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.000159 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
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| 1.3834 | 0.9739 | 14 | 1.1666 | 0.5714 | 0.5553 | |
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| 1.0717 | 1.9478 | 28 | 0.9962 | 0.5952 | 0.5891 | |
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| 0.6143 | 2.9913 | 43 | 0.8219 | 0.7143 | 0.7228 | |
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| 0.4801 | 3.9652 | 57 | 0.8748 | 0.7143 | 0.7138 | |
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| 0.2112 | 4.9391 | 71 | 1.1275 | 0.6905 | 0.6878 | |
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| 0.1627 | 5.9826 | 86 | 1.2672 | 0.6905 | 0.6839 | |
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| 0.118 | 6.9565 | 100 | 1.4471 | 0.6429 | 0.6357 | |
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| 0.088 | 8.0 | 115 | 1.4548 | 0.7143 | 0.7149 | |
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| 0.0674 | 8.9739 | 129 | 1.4981 | 0.6905 | 0.6858 | |
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| 0.0715 | 9.7391 | 140 | 1.4867 | 0.7143 | 0.7126 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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