Tuned_bioBERT

Purpose: We fine-tuned the BioBERT model on the text part of the IU Chest X-ray dataset. Then this model could be used as an embedding model for text embedding to support our experiment on retrieval-augmented in-context learning.

Usage:

• tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-v1.1")
• model = AutoModel.from_pretrained("Learn4everrr/Tuned_bioBERT")

Training parameters: training_args = TrainingArguments( output_dir="./biobert_finetuned", num_train_epochs=30, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir="./logs", logging_steps=10, eval_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, save_total_limit=1, )

Please cite our paper as

@article{zhan2025retrieval,
  title={Retrieval-augmented in-context learning for multimodal large language models in disease classification},
  author={Zhan, Zaifu and Zhou, Shuang and Zhou, Xiaoshan and Xiao, Yongkang and Wang, Jun and Deng, Jiawen and Zhu, He and Hou, Yu and Zhang, Rui},
  journal={arXiv preprint arXiv:2505.02087},
  year={2025}
}