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Training complete
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README.md
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---
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license: mit
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base_model: emilyalsentzer/Bio_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: NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model
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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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# NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model
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This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2634
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- Precision: 0.4559
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- Recall: 0.4616
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- F1: 0.4587
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- Accuracy: 0.9334
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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: 8
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- eval_batch_size: 8
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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: 8
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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 | 71 | 0.2011 | 0.3859 | 0.3371 | 0.3598 | 0.9297 |
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| No log | 2.0 | 142 | 0.1970 | 0.3956 | 0.3894 | 0.3925 | 0.9283 |
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| No log | 3.0 | 213 | 0.2041 | 0.4140 | 0.3977 | 0.4057 | 0.9307 |
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| No log | 4.0 | 284 | 0.2167 | 0.4379 | 0.4110 | 0.4240 | 0.9348 |
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| No log | 5.0 | 355 | 0.2293 | 0.4430 | 0.4159 | 0.4290 | 0.9338 |
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| No log | 6.0 | 426 | 0.2447 | 0.4615 | 0.4375 | 0.4492 | 0.9356 |
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| No log | 7.0 | 497 | 0.2587 | 0.4601 | 0.4500 | 0.4550 | 0.9342 |
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| 0.0861 | 8.0 | 568 | 0.2634 | 0.4559 | 0.4616 | 0.4587 | 0.9334 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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