---
license: mit
library_name: custom
pipeline_tag: text-classification
datasets:
- RKB109/rag-evaluation-lab-20260720-dataset
tags:
- synthetic-data
- transparent-baseline
- ai-evaluation
- text-classification
- question-answering
- text-ranking
- summarization
metrics:
- accuracy
---

# RAG Evaluation Lab Baseline Model

## Model Description

This repository contains a small, transparent prototype model for
**RAG systems often ship without a stable regression set or failure taxonomy.**

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: 0.75
- Intended metrics: failure_class_accuracy, citation_coverage, release_gate_pass_rate

## Intended Use

- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation

## Hugging Face Task Coverage

- `text-classification`
- `question-answering`
- `text-ranking`
- `summarization`

## Limitations and Risks

Synthetic cases validate the harness, not a production RAG system. Teams must add representative domain examples.

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.