File size: 1,513 Bytes
bb6879d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ---
license: mit
library_name: custom
pipeline_tag: text-classification
datasets:
- RKB109/rag-evaluation-lab-20260809-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.
|