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--- |
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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: ClinicalBERT_BioNLP13CG_NER |
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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_BioNLP13CG_NER |
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This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3426 |
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- Precision: 0.7090 |
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- Recall: 0.6958 |
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- F1: 0.7023 |
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- Accuracy: 0.9104 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 0.99 | 95 | 0.4756 | 0.6077 | 0.5579 | 0.5817 | 0.8777 | |
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| No log | 2.0 | 191 | 0.3626 | 0.6999 | 0.6889 | 0.6944 | 0.9068 | |
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| No log | 2.98 | 285 | 0.3426 | 0.7090 | 0.6958 | 0.7023 | 0.9104 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.0 |
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- Tokenizers 0.15.0 |
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