README.md

metadata

pretty_name: Sentence Relevance Extractor (SRE)
license: mit
language:
  - en

Sentence Relevance Extractor (SRE)

Sentence Relevance Extractor (SRE) is a large-scale dataset for binary evidence selection in multi-document, multi-hop question answering.

The goal:

Given a question and a sentence from the context, predict whether this sentence is relevant evidence ("Yes") or irrelevant ("No").

This dataset is suitable for training:

• Sentence-level RAG rerankers
• Binary relevance classifiers
• Optimization-based truth discovery systems
• Multi-hop QA evidence selectors

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Dataset Statistics

Split	# Samples
Train	1,902,056
Validation	211,340
Test	141,726
Total	2,255,122

Dataset Source Summary

• From HF train splits: 2,113,396
• From HF validation/test splits: 141,726
• After balancing & sampling → final splits above.

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Provided Files

• multihop_sentrel_train.jsonl
• multihop_sentrel_val.jsonl
• multihop_sentrel_test.jsonl

Each line corresponds to one (question, sentence) relevance judgment.

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Data Format (JSONL)

Each row:

{
  "dataset": "2wikimultihopqa",
  "source_id": "7f23725...",
  "question": "Who is the child of the director of Inquilaab (2002 film)?",
  "full_context": "Inquilaab ... (titles and sentences)",
  "sentence": "Inquilaab is a 2002 Bengali action thriller film directed by Anup Sengupta.",
  "label": "Yes",
  "title": "Inquilaab (2002 film)",
  "doc_index": 0,
  "sent_index": 0,
  "split": "train"
}