CardioEmbed-E5-large-v2

Domain-specialized cardiology text embeddings using LoRA-adapted E5-large-v2

Part of a comparative study of 10 embedding architectures for clinical cardiology.

Performance

Metric	Score
Separation Score	0.284

Usage

from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

base_model = AutoModel.from_pretrained("intfloat/e5-large-v2")
tokenizer = AutoTokenizer.from_pretrained("intfloat/e5-large-v2")
model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-E5-large-v2")

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

@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}
}