| --- |
| library_name: peft |
| license: mit |
| base_model: emilyalsentzer/Bio_ClinicalBERT |
| tags: |
| - base_model:adapter:emilyalsentzer/Bio_ClinicalBERT |
| - lora |
| - transformers |
| model-index: |
| - name: finetunePathologicalTextUsingBioBERT |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # finetunePathologicalTextUsingBioBERT |
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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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| ## 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: |
| - learning_rate: 0.0005 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - optimizer: Use adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - training_steps: 10000 |
| - mixed_precision_training: Native AMP |
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| ### Training results |
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| ### Framework versions |
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| - PEFT 0.19.1 |
| - Transformers 5.7.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
