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--- |
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library_name: transformers |
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base_model: dmis-lab/biobert-base-cased-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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Biobert_combo_v2 |
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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_combo_v2 |
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1933 |
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- Accuracy: 0.924 |
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- Auc: 0.978 |
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- Precision: 0.938 |
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- Recall: 0.938 |
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- F1: 0.938 |
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- F1-macro: 0.919 |
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- F1-micro: 0.924 |
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- F1-weighted: 0.924 |
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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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| 0.4506 | 0.2661 | 500 | 0.2929 | 0.883 | 0.944 | 0.902 | 0.91 | 0.906 | 0.876 | 0.883 | 0.883 | |
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| 0.2846 | 0.5323 | 1000 | 0.2606 | 0.897 | 0.957 | 0.904 | 0.934 | 0.918 | 0.89 | 0.897 | 0.897 | |
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| 0.2462 | 0.7984 | 1500 | 0.2316 | 0.907 | 0.966 | 0.915 | 0.938 | 0.926 | 0.901 | 0.907 | 0.907 | |
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| 0.2337 | 1.0644 | 2000 | 0.2297 | 0.91 | 0.967 | 0.926 | 0.928 | 0.927 | 0.904 | 0.91 | 0.91 | |
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| 0.21 | 1.3305 | 2500 | 0.2212 | 0.911 | 0.97 | 0.934 | 0.922 | 0.928 | 0.906 | 0.911 | 0.911 | |
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| 0.2033 | 1.5967 | 3000 | 0.2181 | 0.913 | 0.972 | 0.925 | 0.935 | 0.93 | 0.908 | 0.913 | 0.913 | |
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| 0.2029 | 1.8628 | 3500 | 0.2109 | 0.916 | 0.974 | 0.92 | 0.946 | 0.933 | 0.91 | 0.916 | 0.915 | |
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| 0.1948 | 2.1288 | 4000 | 0.2030 | 0.921 | 0.975 | 0.94 | 0.931 | 0.935 | 0.916 | 0.921 | 0.921 | |
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| 0.1812 | 2.3949 | 4500 | 0.2093 | 0.918 | 0.974 | 0.933 | 0.935 | 0.934 | 0.913 | 0.918 | 0.918 | |
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| 0.1822 | 2.6611 | 5000 | 0.1996 | 0.92 | 0.976 | 0.933 | 0.939 | 0.936 | 0.916 | 0.92 | 0.92 | |
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| 0.1818 | 2.9272 | 5500 | 0.1945 | 0.923 | 0.977 | 0.936 | 0.94 | 0.938 | 0.918 | 0.923 | 0.923 | |
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| 0.1707 | 3.1932 | 6000 | 0.1955 | 0.923 | 0.977 | 0.946 | 0.929 | 0.937 | 0.919 | 0.923 | 0.923 | |
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| 0.1635 | 3.4593 | 6500 | 0.2019 | 0.922 | 0.977 | 0.935 | 0.939 | 0.937 | 0.917 | 0.922 | 0.922 | |
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| 0.1747 | 3.7255 | 7000 | 0.1983 | 0.923 | 0.977 | 0.931 | 0.945 | 0.938 | 0.918 | 0.923 | 0.923 | |
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| 0.1735 | 3.9916 | 7500 | 0.1956 | 0.923 | 0.978 | 0.936 | 0.941 | 0.938 | 0.919 | 0.923 | 0.923 | |
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| 0.1646 | 4.2576 | 8000 | 0.1994 | 0.921 | 0.977 | 0.933 | 0.94 | 0.937 | 0.916 | 0.921 | 0.921 | |
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| 0.1616 | 4.5238 | 8500 | 0.1925 | 0.924 | 0.978 | 0.942 | 0.934 | 0.938 | 0.919 | 0.924 | 0.924 | |
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| 0.1615 | 4.7899 | 9000 | 0.1933 | 0.924 | 0.978 | 0.938 | 0.938 | 0.938 | 0.919 | 0.924 | 0.924 | |
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### Framework versions |
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- Transformers 4.53.2 |
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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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