CardioEmbed-BGE-M3

CardioEmbed-BGE-M3

Domain-specialized cardiology text embeddings using LoRA-adapted BGE-M3

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

Performance

Usage

from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

base_model = AutoModel.from_pretrained("BAAI/bge-m3")
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-m3")
model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed-BGE-M3")

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}
}

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Built & maintained by Richard Young · DeepNeuro