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CardioEmbed-E5-large-v2
richardyoung/CardioEmbed-E5-large-v2
CardioEmbed-E5-large-v2 is a sentence similarity model from richardyoung. Use it when you need a score for how close two texts are. It is set up for peft. The card lists the license as apache-2.0.
Domain-specialized cardiology text embeddings using LoRA-adapted E5-large-v2
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At a glance
- Task
- Sentence Similarity
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Nov 24, 2025
- Updated
- Sep 26, 2026
- SHA
- 4a8b7c84
Base models
Model card
From the Hugging Face model README
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}
}
Built & maintained by Richard Young · DeepNeuro
- Task
- Sentence Similarity
- Library
- peft
- License
- apache-2.0
- Languages
- en
- Created
- Nov 24, 2025
- Updated
- Sep 26, 2026