finetunePathologicalTextUsingBioBERT
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
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
Training results
Framework versions
- PEFT 0.19.1
- Transformers 5.7.0
- Pytorch 2.6.0+cu124
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for drkareemkamal/finetunePathologicalTextUsingBioBERT
Base model
emilyalsentzer/Bio_ClinicalBERT
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("emilyalsentzer/Bio_ClinicalBERT") model = PeftModel.from_pretrained(base_model, "drkareemkamal/finetunePathologicalTextUsingBioBERT")