README.md

metadata

license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
size_categories:
  - 1K<n<10K
source_datasets:
  - quality

QuALITY Search Dataset

This dataset is derived from the QuALITY (Question Answering with Long Input Texts, Yes!) dataset, specifically designed for search-based question answering environments.

Dataset Overview

• Source: QuALITY v1.0.1 dev set
• Samples: 453 question-article pairs from 50 articles
• Task: Multiple-choice question answering with search capabilities
• Embeddings: OpenAI text-embedding-3-small (1536 dimensions)

Quick Start

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("bhogan/quality-search-dataset")

# Access a sample
sample = dataset['train'][0]
print(f"Question: {sample['question']}")
print(f"Options: {sample['options']}")
print(f"Correct answer: {sample['gold_label']}")
print(f"Number of chunks: {len(sample['chunks'])}")

Dataset Structure

Each sample contains:

• article_id: Unique identifier for the article
• title: Article title
• author: Article author
• topic: Article topic/category
• year: Publication year
• full_article: Complete cleaned article text
• chunks: Text chunks (~200 characters each with 50 character overlap)
• chunk_embeddings: OpenAI embeddings for each chunk (1536 dimensions)
• question: Multiple choice question
• options: List of 4 answer options
• gold_label: Correct answer (1-4)
• writer_label: Original writer's answer
• question_unique_id: Unique identifier for the question
• difficult: Difficulty indicator

Search Functionality

This dataset is designed for search-based QA where:

1. Agent receives a question about an article
2. Agent can submit search queries (up to 3)
3. System returns top-5 most similar chunks using cosine similarity
4. Agent must answer the multiple choice question using retrieved chunks

Citation

If you use this dataset, please cite the original QuALITY paper:

@inproceedings{pang2021quality,
  title={{QuALITY}: Question Answering with Long Input Texts, Yes!},
  author={Pang, Richard Yuanzhe and Parrish, Alicia and Joshi, Nitish and Nangia, Nikita and Phang, Jason and Chen, Angelica and Padmakumar, Vishakh and Ma, Johnny and Thompson, Jana and He, He and Bowman, Samuel R},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={3626--3637},
  year={2021}
}