
# Dataset Card for XQuAD-XTREME

## 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/deepmind/xquad](https://github.com/deepmind/xquad)
- **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:** 139.53 MB
- **Size of the generated dataset:** 18.09 MB
- **Total amount of disk used:** 157.62 MB

### Dataset Summary

XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.

We also include "translate-train", "translate-dev", and "translate-test"
splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. https://proceedings.mlr.press/v119/hu20b/hu20b.pdf

### 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

#### ar

- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.64 MB
- **Total amount of disk used:** 14.33 MB

An example of 'test' looks as follows.
```
This example was too long and was cropped:

{
    "answers": {
        "answer_start": [527],
        "text": ["136"]
    },
    "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
    "id": "56beb4343aeaaa14008c925c",
    "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```

#### de

- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.23 MB
- **Total amount of disk used:** 13.91 MB

An example of 'test' looks as follows.
```
This example was too long and was cropped:

{
    "answers": {
        "answer_start": [527],
        "text": ["136"]
    },
    "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
    "id": "56beb4343aeaaa14008c925c",
    "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```

#### el

- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 2.11 MB
- **Total amount of disk used:** 14.79 MB

An example of 'test' looks as follows.
```
This example was too long and was cropped:

{
    "answers": {
        "answer_start": [527],
        "text": ["136"]
    },
    "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
    "id": "56beb4343aeaaa14008c925c",
    "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```

#### en

- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.07 MB
- **Total amount of disk used:** 13.75 MB

An example of 'test' looks as follows.
```
This example was too long and was cropped:

{
    "answers": {
        "answer_start": [527],
        "text": ["136"]
    },
    "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
    "id": "56beb4343aeaaa14008c925c",
    "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```

#### es

- **Size of downloaded dataset files:** 12.68 MB
- **Size of the generated dataset:** 1.22 MB
- **Total amount of disk used:** 13.90 MB

An example of 'test' looks as follows.
```
This example was too long and was cropped:

{
    "answers": {
        "answer_start": [527],
        "text": ["136"]
    },
    "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...",
    "id": "56beb4343aeaaa14008c925c",
    "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?"
}
```

### Data Fields

The data fields are the same among all splits.

#### ar
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

#### de
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

#### el
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

#### en
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

#### es
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.

### Data Splits

| name     | validation |
| -------- | ---------: |
| ar |       1190 |
| de |       1190 |
| el |       1190 |
| en |       1190 |
| es |       1190 |

## 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{Artetxe:etal:2019,
      author    = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
      title     = {On the cross-lingual transferability of monolingual representations},
      journal   = {CoRR},
      volume    = {abs/1910.11856},
      year      = {2019},
      archivePrefix = {arXiv},
      eprint    = {1910.11856}
}

```


### Contributions

Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
task_categories:question-answering task_ids:extractive-qa annotations_creators:expert-generated language_creators:expert-generated multilinguality:multilingual size_categories:unknown source_datasets:extended|squad language:en language:es language:de language:el language:hi language:th language:ru language:tr language:ar language:vi language:zh language:ro license:cc-by-sa-4.0 arxiv:1910.11856 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en", "es", "de", "el", "hi", "th", "ru", "tr", "ar", "vi", "zh", "ro"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": ["extended|squad"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "paperswithcode_id": "xquad", "pretty_name": "XQuAD-XTREME"}
TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|squad #language-English #language-Spanish #language-German #language-Modern Greek (1453-) #language-Hindi #language-Thai #language-Russian #language-Turkish #language-Arabic #language-Vietnamese #language-Chinese #language-Romanian #license-cc-by-sa-4.0 #arxiv-1910.11856 #region-us 

Dataset Card for XQuAD-XTREME
=============================


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: 139.53 MB
* Size of the generated dataset: 18.09 MB
* Total amount of disk used: 157.62 MB


### Dataset Summary


XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.


We also include "translate-train", "translate-dev", and "translate-test"
splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. URL


### Supported Tasks and Leaderboards


### Languages


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


### Data Instances


#### ar


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.64 MB
* Total amount of disk used: 14.33 MB


An example of 'test' looks as follows.


#### de


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.23 MB
* Total amount of disk used: 13.91 MB


An example of 'test' looks as follows.


#### el


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 2.11 MB
* Total amount of disk used: 14.79 MB


An example of 'test' looks as follows.


#### en


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.07 MB
* Total amount of disk used: 13.75 MB


An example of 'test' looks as follows.


#### es


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.22 MB
* Total amount of disk used: 13.90 MB


An example of 'test' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### ar


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.


#### de


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.


#### el


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.


#### en


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.


#### es


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' 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 @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.



### Dataset Summary


XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.


We also include "translate-train", "translate-dev", and "translate-test"
splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. URL ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### ar


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.64 MB
* Total amount of disk used: 14.33 MB


An example of 'test' looks as follows. #### de


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.23 MB
* Total amount of disk used: 13.91 MB


An example of 'test' looks as follows. #### el


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 2.11 MB
* Total amount of disk used: 14.79 MB


An example of 'test' looks as follows. #### en


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.07 MB
* Total amount of disk used: 13.75 MB


An example of 'test' looks as follows. #### es


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.22 MB
* Total amount of disk used: 13.90 MB


An example of 'test' looks as follows. ### Data Fields


The data fields are the same among all splits. #### ar


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### de


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### el


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### en


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### es


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' 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 @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|squad #language-English #language-Spanish #language-German #language-Modern Greek (1453-) #language-Hindi #language-Thai #language-Russian #language-Turkish #language-Arabic #language-Vietnamese #language-Chinese #language-Romanian #license-cc-by-sa-4.0 #arxiv-1910.11856 #region-us 
 ### Dataset Summary


XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.


We also include "translate-train", "translate-dev", and "translate-test"
splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. URL ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### ar


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.64 MB
* Total amount of disk used: 14.33 MB


An example of 'test' looks as follows. #### de


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.23 MB
* Total amount of disk used: 13.91 MB


An example of 'test' looks as follows. #### el


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 2.11 MB
* Total amount of disk used: 14.79 MB


An example of 'test' looks as follows. #### en


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.07 MB
* Total amount of disk used: 13.75 MB


An example of 'test' looks as follows. #### es


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.22 MB
* Total amount of disk used: 13.90 MB


An example of 'test' looks as follows. ### Data Fields


The data fields are the same among all splits. #### ar


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### de


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### el


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### en


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. #### es


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' 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 @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #source_datasets-extended|squad #language-English #language-Spanish #language-German #language-Modern Greek (1453-) #language-Hindi #language-Thai #language-Russian #language-Turkish #language-Arabic #language-Vietnamese #language-Chinese #language-Romanian #license-cc-by-sa-4.0 #arxiv-1910.11856 #region-us 
### Dataset Summary


XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages.


We also include "translate-train", "translate-dev", and "translate-test"
splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. URL### Supported Tasks and Leaderboards### Languages


Dataset Structure
-----------------### Data Instances#### ar


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.64 MB
* Total amount of disk used: 14.33 MB


An example of 'test' looks as follows.#### de


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.23 MB
* Total amount of disk used: 13.91 MB


An example of 'test' looks as follows. passage: #### el


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 2.11 MB
* Total amount of disk used: 14.79 MB


An example of 'test' looks as follows.#### en


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.07 MB
* Total amount of disk used: 13.75 MB


An example of 'test' looks as follows.#### es


* Size of downloaded dataset files: 12.68 MB
* Size of the generated dataset: 1.22 MB
* Total amount of disk used: 13.90 MB


An example of 'test' looks as follows.### Data Fields


The data fields are the same among all splits.#### ar


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.#### de


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.#### el


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.#### en


* 'id': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.