README.md

metadata

license: mit
task_categories:
  - question-answering
  - sentence-similarity
language:
  - en
tags:
  - retrieval
  - rag
  - reranking
  - information-retrieval
pretty_name: Tiny RAG with Reranking (eval set + chunk sample)
size_categories:
  - n<1K

tiny-rag-with-reranking

A tiny retrieval / question-answering evaluation set plus a sample of a chunked public-domain corpus, used by the tiny-rag-with-reranking RAG pipeline (bi-encoder retrieval + cross-encoder reranking, with a chunk-size sweep).

Task

Question answering / passage retrieval. Each query carries one or more short answer substrings; a retrieved passage is judged relevant if it contains any answer substring (case- and whitespace-insensitive). This substring labeling keeps relevance valid regardless of how the corpus is chunked.

Files

• qa.json — the evaluation set: 12 queries, each with a question, one or more answers (relevant-passage substrings), and the source document.
• chunks_sample.json — a 60-chunk sample (of 902 total in the full build) of the chunked corpus, each with doc, character offsets start/end, and text. Produced by the adaptive chunker at target size 128.

Generation method

The corpus is public-domain plaintext from Project Gutenberg (Alice's Adventures in Wonderland, The Time Machine, A Study in Scarlet), with license headers and footers stripped, then chunked with an adaptive sentence-merging strategy. The query/relevant-passage labels in qa.json are hand-written: canonical phrases drawn verbatim from the corpus so they stay stable across chunkings.

Measured results (small-scale benchmark, single RTX 5090)

Bi-encoder all-MiniLM-L6-v2 retrieval vs adding cross-encoder ms-marco-MiniLM-L-6-v2 reranking, retrieve top-20, metric @k=5:

stage	precision@5	recall@5
bi-encoder only	0.1333	0.0650
+ cross-encoder rerank	0.2000	0.1394

Chunk-size sweep best size by precision@5: 512.

License

MIT. Underlying source texts are public domain (Project Gutenberg).