README.md

---
library_name: peft
base_model: sentence-transformers/all-mpnet-base-v2
tags:
- medical
- cardiology
- embeddings
- domain-adaptation
- lora
- sentence-transformers
- sentence-similarity
language:
- en
license: apache-2.0
---

# CardioEmbed-MPNet-base

**Domain-specialized cardiology text embeddings using LoRA-adapted MPNet-base**

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

## Performance

| Metric | Score |
|--------|-------|
| Separation Score | **0.386** |

## Usage

```python
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}
}
```