kyunghyuncho/search_qa

---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: SearchQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: searchqa
dataset_info:
- config_name: raw_jeopardy
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: string
- name: answer
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
- name: search_results
sequence:
- name: urls
dtype: string
- name: snippets
dtype: string
- name: titles
dtype: string
- name: related_links
dtype: string
splits:
- name: train
num_bytes: 7770972348
num_examples: 216757
download_size: 3314386157
dataset_size: 7770972348
- config_name: train_test_val
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: string
- name: answer
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
- name: search_results
sequence:
- name: urls
dtype: string
- name: snippets
dtype: string
- name: titles
dtype: string
- name: related_links
dtype: string
splits:
- name: train
num_bytes: 5303005740
num_examples: 151295
- name: test
num_bytes: 1466749978
num_examples: 43228
- name: validation
num_bytes: 740962715
num_examples: 21613
download_size: 3148550732
dataset_size: 7510718433
---

# Dataset Card for "search_qa"

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Repository:** https://github.com/nyu-dl/dl4ir-searchQA
- **Paper:** [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://arxiv.org/abs/1704.05179)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 6.46 GB
- **Size of the generated dataset:** 15.28 GB
- **Total amount of disk used:** 21.74 GB

### Dataset Summary

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind
CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article
and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google.
Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context
tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation
as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human
and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.

### Supported Tasks and Leaderboards

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Dataset Structure

### Data Instances

#### raw_jeopardy

- **Size of downloaded dataset files:** 3.31 GB
- **Size of the generated dataset:** 7.77 GB
- **Total amount of disk used:** 11.09 GB

An example of 'train' looks as follows.
```

```

#### train_test_val

- **Size of downloaded dataset files:** 3.15 GB
- **Size of the generated dataset:** 7.51 GB
- **Total amount of disk used:** 10.66 GB

An example of 'validation' looks as follows.
```

```

### Data Fields

The data fields are the same among all splits.

#### raw_jeopardy
- `category`: a `string` feature.
- `air_date`: a `string` feature.
- `question`: a `string` feature.
- `value`: a `string` feature.
- `answer`: a `string` feature.
- `round`: a `string` feature.
- `show_number`: a `int32` feature.
- `search_results`: a dictionary feature containing:
- `urls`: a `string` feature.
- `snippets`: a `string` feature.
- `titles`: a `string` feature.
- `related_links`: a `string` feature.

#### train_test_val
- `category`: a `string` feature.
- `air_date`: a `string` feature.
- `question`: a `string` feature.
- `value`: a `string` feature.
- `answer`: a `string` feature.
- `round`: a `string` feature.
- `show_number`: a `int32` feature.
- `search_results`: a dictionary feature containing:
- `urls`: a `string` feature.
- `snippets`: a `string` feature.
- `titles`: a `string` feature.
- `related_links`: a `string` feature.

### Data Splits

#### raw_jeopardy

| |train |
|------------|-----:|
|raw_jeopardy|216757|

#### train_test_val

| |train |validation|test |
|--------------|-----:|---------:|----:|
|train_test_val|151295| 21613|43228|

## Dataset Creation

### Curation Rationale

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Citation Information

```
@article{DBLP:journals/corr/DunnSHGCC17,
author = {Matthew Dunn and
Levent Sagun and
Mike Higgins and
V. Ugur G{"{u}}ney and
Volkan Cirik and
Kyunghyun Cho},
title = {SearchQA: {A} New Q{\&}A Dataset Augmented with Context from a
Search Engine},
journal = {CoRR},
volume = {abs/1704.05179},
year = {2017},
url = {http://arxiv.org/abs/1704.05179},
archivePrefix = {arXiv},
eprint = {1704.05179},
timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/DunnSHGCC17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

### Contributions

Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

原始信息汇总

数据集概述

基本信息

• 名称: SearchQA
• 语言: 英语（en）
• 多语言性: 单语
• 许可证: 未知
• 规模: 100K<n<1M
• 源数据: 原始数据
• 任务类别: 问答（question-answering）
• 任务ID: extractive-qa
• 论文代码ID: searchqa

数据集结构

• 配置名称: raw_jeopardy 和 train_test_val

• 特征:

  • category: 字符串
  • air_date: 字符串
  • question: 字符串
  • value: 字符串
  • answer: 字符串
  • round: 字符串
  • show_number: 整数（int32）
  • search_results: 字典，包含 urls, snippets, titles, related_links，均为字符串类型

• 数据分割:

  • raw_jeopardy:
    • train: 216757 个示例，占用 7770972348 字节
  • train_test_val:
    • train: 151295 个示例，占用 5303005740 字节
    • test: 43228 个示例，占用 1466749978 字节
    • validation: 21613 个示例，占用 740962715 字节

数据集大小

• 下载大小:
  • raw_jeopardy: 3314386157 字节
  • train_test_val: 3148550732 字节
• 生成数据集大小:
  • raw_jeopardy: 7770972348 字节
  • train_test_val: 7510718433 字节