Datasets:

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.