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Datasets:
facebook
/
mlqa

Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
English
German
Spanish
Size:
10K<n<100K
License:
Dataset card Files Community
3
mlqa / README.md
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---
pretty_name: MLQA (MultiLingual Question Answering)
language:
- en
- de
- es
- ar
- zh
- vi
- hi
license:
- cc-by-sa-3.0
source_datasets:
- original
size_categories:
- 10K<n<100K
language_creators:
- crowdsourced
annotations_creators:
- crowdsourced
multilinguality:
- multilingual
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: mlqa
dataset_info:
- config_name: mlqa-translate-train.ar
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---
# Dataset Card for "mlqa"
## 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
- **Homepage:** [https://github.com/facebookresearch/MLQA](https://github.com/facebookresearch/MLQA)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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:** 4.15 GB
- **Size of the generated dataset:** 910.01 MB
- **Total amount of disk used:** 5.06 GB
### Dataset Summary
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.
## Dataset Structure
### Data Instances
#### mlqa-translate-test.ar
- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 5.48 MB
- **Total amount of disk used:** 15.56 MB
An example of 'test' looks as follows.
```
```
#### mlqa-translate-test.de
- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 3.88 MB
- **Total amount of disk used:** 13.96 MB
An example of 'test' looks as follows.
```
```
#### mlqa-translate-test.es
- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 3.92 MB
- **Total amount of disk used:** 13.99 MB
An example of 'test' looks as follows.
```
```
#### mlqa-translate-test.hi
- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 4.61 MB
- **Total amount of disk used:** 14.68 MB
An example of 'test' looks as follows.
```
```
#### mlqa-translate-test.vi
- **Size of downloaded dataset files:** 10.08 MB
- **Size of the generated dataset:** 6.00 MB
- **Total amount of disk used:** 16.07 MB
An example of 'test' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### mlqa-translate-test.ar
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `answer_start`: a `int32` feature.
- `text`: a `string` feature.
- `id`: a `string` feature.
#### mlqa-translate-test.de
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `answer_start`: a `int32` feature.
- `text`: a `string` feature.
- `id`: a `string` feature.
#### mlqa-translate-test.es
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `answer_start`: a `int32` feature.
- `text`: a `string` feature.
- `id`: a `string` feature.
#### mlqa-translate-test.hi
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `answer_start`: a `int32` feature.
- `text`: a `string` feature.
- `id`: a `string` feature.
#### mlqa-translate-test.vi
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `answer_start`: a `int32` feature.
- `text`: a `string` feature.
- `id`: a `string` feature.
### Data Splits
| name |test|
|----------------------|---:|
|mlqa-translate-test.ar|5335|
|mlqa-translate-test.de|4517|
|mlqa-translate-test.es|5253|
|mlqa-translate-test.hi|4918|
|mlqa-translate-test.vi|5495|
## 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{lewis2019mlqa,
title = {MLQA: Evaluating Cross-lingual Extractive Question Answering},
author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
journal = {arXiv preprint arXiv:1910.07475},
year = 2019,
eid = {arXiv: 1910.07475}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@M-Salti](https://github.com/M-Salti), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.