README.md

metadata

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.