University of Nevada Las Vegas / Concorde Career Colleges (Young & Matthews)
Domain-specialized text embedding model for clinical cardiology, built by fine-tuning the Qwen3-Embedding-8B language model with LoRA adapters via contrastive learning on cardiology textbook sentences. Reaches 99.60% top-1 accuracy on cardiology-specific semantic retrieval, nearly 16 points above the prior MedTE baseline. The training corpus draws on roughly 150,000 sentences from seven copyrighted textbooks and is not public, though the resulting model weights are freely downloadable.
Architecture
LLM
Qwen3-Embedding-8B decoder-only LLM backbone, LoRA fine-tuned with InfoNCE contrastive loss and in-batch negatives
Framework
PyTorch
Added to catalog
2026-07-10
Apache 2.0
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
Cardiology Textbook Sentence-Pair Corpus (CardioEmbed family)
~150,000 anchor-positive sentence pairs derived from 7 cardiology textbooks; text corpus, not human-subject data
Cardiology semantic retrieval (Acc@1)
Embedding