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---
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license: mit
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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: ClinicalTextV4
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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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# ClinicalTextV4
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This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5609
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- Accuracy: 0.8658
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- Precision: 0.8371
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- Recall: 0.8939
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- F1: 0.8646
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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: 1e-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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- num_epochs: 10
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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 | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.4824 | 1.0 | 600 | 0.3630 | 0.8458 | 0.825 | 0.8609 | 0.8426 |
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| 0.3314 | 2.0 | 1200 | 0.3583 | 0.8558 | 0.8252 | 0.8870 | 0.8550 |
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| 0.2673 | 3.0 | 1800 | 0.3437 | 0.8583 | 0.8189 | 0.9043 | 0.8595 |
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| 0.2255 | 4.0 | 2400 | 0.3678 | 0.8675 | 0.8302 | 0.9096 | 0.8680 |
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| 0.1883 | 5.0 | 3000 | 0.4002 | 0.8642 | 0.8259 | 0.9078 | 0.8650 |
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| 0.1562 | 6.0 | 3600 | 0.4695 | 0.8633 | 0.8352 | 0.8904 | 0.8620 |
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| 0.1372 | 7.0 | 4200 | 0.4940 | 0.8658 | 0.8371 | 0.8939 | 0.8646 |
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| 0.1269 | 8.0 | 4800 | 0.5376 | 0.865 | 0.8402 | 0.8870 | 0.8629 |
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| 0.1097 | 9.0 | 5400 | 0.5539 | 0.8633 | 0.8397 | 0.8835 | 0.8610 |
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| 0.0997 | 10.0 | 6000 | 0.5609 | 0.8658 | 0.8371 | 0.8939 | 0.8646 |
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### Framework versions
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- Transformers 4.21.2
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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