Clinical RAG Safety Gateway Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Clinical assistants need retrieval, source attribution, and explicit abstention before answers reach care teams.
The model combines per-label token weights with IDF-weighted evidence
retrieval. It was generated for reproducible architecture demonstrations and
does not call a hosted LLM.
Evaluation
- Held-out synthetic examples: 4
- Accuracy: 1
- Intended metrics: retrieval_accuracy, abstention_coverage, citation_coverage
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
question-answeringsentence-similaritytext-classificationsummarization
Limitations and Risks
Synthetic educational data only. The baseline must not provide diagnosis, treatment, or emergency medical advice.
The dataset is synthetic and small. Do not use this model for consequential
decisions without representative data, expert review, and production-grade
evaluation.
Reproducibility
The linked GitHub repository includes train.py, the exact dataset split,
evaluation code, and the model JSON format.
