
# 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.
task_categories:question-answering task_ids:extractive-qa annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:multilingual size_categories:10K<n<100K source_datasets:original language:en language:de language:es language:ar language:zh language:vi language:hi license:cc-by-sa-3.0 region:us
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TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-German #language-Spanish #language-Arabic #language-Chinese #language-Vietnamese #language-Hindi #license-cc-by-sa-3.0 #region-us 

Dataset Card for "mlqa"
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper:
* Point of Contact:
* 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


### Languages


MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.


Dataset Structure
-----------------


### Data Instances


#### URL


* 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.


#### URL


* 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.


#### URL


* 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.


#### URL


* 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.


#### URL


* 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.


#### URL


* '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.


#### URL


* '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.


#### URL


* '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.


#### URL


* '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.


#### URL


* '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



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.



### 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 ### Languages


MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.


Dataset Structure
----------------- ### Data Instances #### URL


* 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. #### URL


* 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. #### URL


* 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. #### URL


* 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. #### URL


* 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. #### URL


* '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. #### URL


* '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. #### URL


* '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. #### URL


* '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. #### URL


* '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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-German #language-Spanish #language-Arabic #language-Chinese #language-Vietnamese #language-Hindi #license-cc-by-sa-3.0 #region-us 
 ### 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 ### Languages


MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.


Dataset Structure
----------------- ### Data Instances #### URL


* 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. #### URL


* 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. #### URL


* 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. #### URL


* 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. #### URL


* 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. #### URL


* '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. #### URL


* '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. #### URL


* '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. #### URL


* '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. #### URL


* '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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10K<n<100K #source_datasets-original #language-English #language-German #language-Spanish #language-Arabic #language-Chinese #language-Vietnamese #language-Hindi #license-cc-by-sa-3.0 #region-us 
### 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### Languages


MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.


Dataset Structure
-----------------### Data Instances#### URL


* 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.#### URL


* 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.#### URL


* 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.#### URL


* 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. passage: #### URL


* 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.#### URL


* '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.#### URL


* '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.#### URL


* '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.#### URL


* '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.#### URL


* '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



Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------### Social Impact of Dataset### Discussion of Biases