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End of training
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README.md
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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_CRAFT_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_CRAFT_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: 0.1735
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- Precision: 0.7738
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- Recall: 0.7536
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- F1: 0.7636
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- Accuracy: 0.9553
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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 | 1.0 | 347 | 0.1980 | 0.7224 | 0.7239 | 0.7232 | 0.9457 |
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| 0.2292 | 2.0 | 695 | 0.1771 | 0.7528 | 0.7545 | 0.7537 | 0.9530 |
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| 0.0815 | 3.0 | 1041 | 0.1735 | 0.7738 | 0.7536 | 0.7636 | 0.9553 |
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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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