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--- |
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library_name: transformers |
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base_model: dmis-lab/biobert-base-cased-v1.2 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Biobert_fnir |
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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_fnir |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0113 |
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- Accuracy: 0.998 |
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- Auc: 1.0 |
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- Precision: 1.0 |
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- Recall: 0.996 |
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- F1: 0.998 |
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- F1-macro: 0.998 |
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- F1-micro: 0.998 |
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- F1-weighted: 0.998 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.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: 5 |
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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 | Auc | Precision | Recall | F1 | F1-macro | F1-micro | F1-weighted | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:| |
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| 0.0666 | 0.6024 | 100 | 0.0142 | 0.997 | 1.0 | 1.0 | 0.995 | 0.997 | 0.997 | 0.997 | 0.997 | |
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| 0.0146 | 1.2048 | 200 | 0.0124 | 0.997 | 1.0 | 1.0 | 0.993 | 0.997 | 0.997 | 0.997 | 0.997 | |
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| 0.0006 | 1.8072 | 300 | 0.0123 | 0.998 | 1.0 | 1.0 | 0.996 | 0.998 | 0.998 | 0.998 | 0.998 | |
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| 0.0045 | 2.4096 | 400 | 0.0134 | 0.997 | 1.0 | 0.999 | 0.996 | 0.997 | 0.997 | 0.997 | 0.997 | |
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| 0.0028 | 3.0120 | 500 | 0.0116 | 0.998 | 1.0 | 1.0 | 0.996 | 0.998 | 0.998 | 0.998 | 0.998 | |
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| 0.0025 | 3.6145 | 600 | 0.0131 | 0.998 | 1.0 | 1.0 | 0.996 | 0.998 | 0.998 | 0.998 | 0.998 | |
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| 0.003 | 4.2169 | 700 | 0.0104 | 0.998 | 1.0 | 1.0 | 0.996 | 0.998 | 0.998 | 0.998 | 0.998 | |
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| 0.0002 | 4.8193 | 800 | 0.0113 | 0.998 | 1.0 | 1.0 | 0.996 | 0.998 | 0.998 | 0.998 | 0.998 | |
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### Framework versions |
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- Transformers 4.53.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.2 |
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