from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

base_model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2")
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-mpnet-base-v2")
model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-MPNet-base")

Training

Training Data: 106,535 cardiology text pairs from medical textbooks
Method: LoRA fine-tuning (r=16, alpha=32)
Loss: Multiple Negatives Ranking Loss (InfoNCE)

Citation

bibtex

@article{young2024comparative,
  title={Comparative Analysis of LoRA-Adapted Embedding Models for Clinical Cardiology Text Representation},
  author={Young, Richard J and Matthews, Alice M},
  journal={arXiv preprint},
  year={2024}
}