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base_model: medicalai/ClinicalBERT |
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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: ClinicalBERT-medical-text-classification |
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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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# ClinicalBERT-medical-text-classification |
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This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4453 |
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- Accuracy: 0.284 |
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- Precision: 0.1812 |
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- Recall: 0.284 |
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- F1: 0.2132 |
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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: 5e-05 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 2.9605 | 1.0 | 250 | 2.9477 | 0.221 | 0.0488 | 0.221 | 0.0800 | |
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| 2.5952 | 2.0 | 500 | 2.5658 | 0.341 | 0.1400 | 0.341 | 0.1958 | |
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| 2.5191 | 3.0 | 750 | 2.4897 | 0.355 | 0.1531 | 0.355 | 0.2046 | |
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| 2.414 | 4.0 | 1000 | 2.5463 | 0.323 | 0.1913 | 0.323 | 0.1902 | |
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| 2.2946 | 5.0 | 1250 | 2.4793 | 0.347 | 0.1461 | 0.347 | 0.2023 | |
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| 2.4065 | 6.0 | 1500 | 2.4471 | 0.349 | 0.1684 | 0.349 | 0.2198 | |
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| 2.3267 | 7.0 | 1750 | 2.4408 | 0.344 | 0.1672 | 0.344 | 0.2193 | |
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| 2.3173 | 8.0 | 2000 | 2.4214 | 0.358 | 0.1748 | 0.358 | 0.2286 | |
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| 2.1692 | 9.0 | 2250 | 2.4358 | 0.339 | 0.1638 | 0.339 | 0.2147 | |
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| 2.029 | 10.0 | 2500 | 2.4074 | 0.338 | 0.1658 | 0.338 | 0.2178 | |
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| 2.125 | 11.0 | 2750 | 2.3605 | 0.334 | 0.1756 | 0.334 | 0.2239 | |
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| 1.9541 | 12.0 | 3000 | 2.3997 | 0.326 | 0.1623 | 0.326 | 0.2123 | |
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| 2.1619 | 13.0 | 3250 | 2.4450 | 0.321 | 0.1765 | 0.321 | 0.2127 | |
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| 2.101 | 14.0 | 3500 | 2.4453 | 0.284 | 0.1812 | 0.284 | 0.2132 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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