
# Dataset Card for "xnli"

## 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://www.nyu.edu/projects/bowman/xnli/](https://www.nyu.edu/projects/bowman/xnli/)
- **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:** 7.74 GB
- **Size of the generated dataset:** 3.23 GB
- **Total amount of disk used:** 10.97 GB

### Dataset Summary

XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).

### 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

#### all_languages

- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 1.61 GB
- **Total amount of disk used:** 2.09 GB

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

{
    "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...",
    "label": 0,
    "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..."
}
```

#### ar

- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 109.32 MB
- **Total amount of disk used:** 593.29 MB

An example of 'validation' looks as follows.
```
{
    "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.",
    "label": 1,
    "premise": "وقال، ماما، لقد عدت للمنزل."
}
```

#### bg

- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 128.32 MB
- **Total amount of disk used:** 612.28 MB

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

{
    "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...",
    "label": 0,
    "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..."
}
```

#### de

- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 86.17 MB
- **Total amount of disk used:** 570.14 MB

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

{
    "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .",
    "label": 0,
    "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..."
}
```

#### el

- **Size of downloaded dataset files:** 483.96 MB
- **Size of the generated dataset:** 142.30 MB
- **Total amount of disk used:** 626.26 MB

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

{
    "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...",
    "label": 1,
    "premise": "Και είπε, Μαμά, έφτασα στο σπίτι."
}
```

### Data Fields

The data fields are the same among all splits.

#### all_languages
- `premise`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`.
- `hypothesis`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).

#### ar
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).

#### bg
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).

#### de
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).

#### el
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).

### Data Splits

|    name     |train |validation|test|
|-------------|-----:|---------:|---:|
|all_languages|392702|      2490|5010|
|ar           |392702|      2490|5010|
|bg           |392702|      2490|5010|
|de           |392702|      2490|5010|
|el           |392702|      2490|5010|

## 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

```
@InProceedings{conneau2018xnli,
  author = {Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin},
  title = {XNLI: Evaluating Cross-lingual Sentence Representations},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing},
  year = {2018},
  publisher = {Association for Computational Linguistics},
  location = {Brussels, Belgium},
}
```


### Contributions

Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
language:ar language:bg language:de language:el language:en language:es language:fr language:hi language:ru language:sw language:th language:tr language:ur language:vi language:zh region:us
{"language": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "paperswithcode_id": "xnli", "pretty_name": "Cross-lingual Natural Language Inference", "dataset_info": [{"config_name": "all_languages", "features": [{"name": "premise", "dtype": {"translation": {"languages": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"]}}}, {"name": "hypothesis", "dtype": {"translation_variable_languages": {"languages": ["ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "num_languages": 15}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 1581471691, "num_examples": 392702}, {"name": "test", "num_bytes": 19387432, "num_examples": 5010}, {"name": "validation", "num_bytes": 9566179, "num_examples": 2490}], "download_size": 963942271, "dataset_size": 1610425302}, {"config_name": "ar", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 107399614, "num_examples": 392702}, {"name": "test", "num_bytes": 1294553, "num_examples": 5010}, {"name": "validation", "num_bytes": 633001, "num_examples": 2490}], "download_size": 59215902, "dataset_size": 109327168}, {"config_name": "bg", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 125973225, "num_examples": 392702}, {"name": "test", "num_bytes": 1573034, "num_examples": 5010}, {"name": "validation", "num_bytes": 774061, "num_examples": 2490}], "download_size": 66117878, "dataset_size": 128320320}, {"config_name": "de", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 84684140, "num_examples": 392702}, {"name": "test", "num_bytes": 996488, "num_examples": 5010}, {"name": "validation", "num_bytes": 494604, "num_examples": 2490}], "download_size": 55973883, "dataset_size": 86175232}, {"config_name": "el", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 139753358, "num_examples": 392702}, {"name": "test", "num_bytes": 1704785, "num_examples": 5010}, {"name": "validation", "num_bytes": 841226, "num_examples": 2490}], "download_size": 74551247, "dataset_size": 142299369}, {"config_name": "en", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 74444026, "num_examples": 392702}, {"name": "test", "num_bytes": 875134, "num_examples": 5010}, {"name": "validation", "num_bytes": 433463, "num_examples": 2490}], "download_size": 50627367, "dataset_size": 75752623}, {"config_name": "es", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 81383284, "num_examples": 392702}, {"name": "test", "num_bytes": 969813, "num_examples": 5010}, {"name": "validation", "num_bytes": 478422, "num_examples": 2490}], "download_size": 53677157, "dataset_size": 82831519}, {"config_name": "fr", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 85808779, "num_examples": 392702}, {"name": "test", "num_bytes": 1029239, "num_examples": 5010}, {"name": "validation", "num_bytes": 510104, "num_examples": 2490}], "download_size": 55968680, "dataset_size": 87348122}, {"config_name": "hi", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 170593964, "num_examples": 392702}, {"name": "test", "num_bytes": 2073073, "num_examples": 5010}, {"name": "validation", "num_bytes": 1023915, "num_examples": 2490}], "download_size": 70908548, "dataset_size": 173690952}, {"config_name": "ru", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 129859615, "num_examples": 392702}, {"name": "test", "num_bytes": 1603466, "num_examples": 5010}, {"name": "validation", "num_bytes": 786442, "num_examples": 2490}], "download_size": 70702606, "dataset_size": 132249523}, {"config_name": "sw", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 69285725, "num_examples": 392702}, {"name": "test", "num_bytes": 871651, "num_examples": 5010}, {"name": "validation", "num_bytes": 429850, "num_examples": 2490}], "download_size": 45564152, "dataset_size": 70587226}, {"config_name": "th", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 176062892, "num_examples": 392702}, {"name": "test", "num_bytes": 2147015, "num_examples": 5010}, {"name": "validation", "num_bytes": 1061160, "num_examples": 2490}], "download_size": 77222045, "dataset_size": 179271067}, {"config_name": "tr", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 71637140, "num_examples": 392702}, {"name": "test", "num_bytes": 934934, "num_examples": 5010}, {"name": "validation", "num_bytes": 459308, "num_examples": 2490}], "download_size": 48509680, "dataset_size": 73031382}, {"config_name": "ur", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 96441486, "num_examples": 392702}, {"name": "test", "num_bytes": 1416241, "num_examples": 5010}, {"name": "validation", "num_bytes": 699952, "num_examples": 2490}], "download_size": 46682785, "dataset_size": 98557679}, {"config_name": "vi", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 101417430, "num_examples": 392702}, {"name": "test", "num_bytes": 1190217, "num_examples": 5010}, {"name": "validation", "num_bytes": 590680, "num_examples": 2490}], "download_size": 57690058, "dataset_size": 103198327}, {"config_name": "zh", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 72224841, "num_examples": 392702}, {"name": "test", "num_bytes": 777929, "num_examples": 5010}, {"name": "validation", "num_bytes": 384851, "num_examples": 2490}], "download_size": 48269855, "dataset_size": 73387621}], "configs": [{"config_name": "all_languages", "data_files": [{"split": "train", "path": "all_languages/train-*"}, {"split": "test", "path": "all_languages/test-*"}, {"split": "validation", "path": "all_languages/validation-*"}]}, {"config_name": "ar", "data_files": [{"split": "train", "path": "ar/train-*"}, {"split": "test", "path": "ar/test-*"}, {"split": "validation", "path": "ar/validation-*"}]}, {"config_name": "bg", "data_files": [{"split": "train", "path": "bg/train-*"}, {"split": "test", "path": "bg/test-*"}, {"split": "validation", "path": "bg/validation-*"}]}, {"config_name": "de", "data_files": [{"split": "train", "path": "de/train-*"}, {"split": "test", "path": "de/test-*"}, {"split": "validation", "path": "de/validation-*"}]}, {"config_name": "el", "data_files": [{"split": "train", "path": "el/train-*"}, {"split": "test", "path": "el/test-*"}, {"split": "validation", "path": "el/validation-*"}]}, {"config_name": "en", "data_files": [{"split": "train", "path": "en/train-*"}, {"split": "test", "path": "en/test-*"}, {"split": "validation", "path": "en/validation-*"}]}, {"config_name": "es", "data_files": [{"split": "train", "path": "es/train-*"}, {"split": "test", "path": "es/test-*"}, {"split": "validation", "path": "es/validation-*"}]}, {"config_name": "fr", "data_files": [{"split": "train", "path": "fr/train-*"}, {"split": "test", "path": "fr/test-*"}, {"split": "validation", "path": "fr/validation-*"}]}, {"config_name": "hi", "data_files": [{"split": "train", "path": "hi/train-*"}, {"split": "test", "path": "hi/test-*"}, {"split": "validation", "path": "hi/validation-*"}]}, {"config_name": "ru", "data_files": [{"split": "train", "path": "ru/train-*"}, {"split": "test", "path": "ru/test-*"}, {"split": "validation", "path": "ru/validation-*"}]}, {"config_name": "sw", "data_files": [{"split": "train", "path": "sw/train-*"}, {"split": "test", "path": "sw/test-*"}, {"split": "validation", "path": "sw/validation-*"}]}, {"config_name": "th", "data_files": [{"split": "train", "path": "th/train-*"}, {"split": "test", "path": "th/test-*"}, {"split": "validation", "path": "th/validation-*"}]}, {"config_name": "tr", "data_files": [{"split": "train", "path": "tr/train-*"}, {"split": "test", "path": "tr/test-*"}, {"split": "validation", "path": "tr/validation-*"}]}, {"config_name": "ur", "data_files": [{"split": "train", "path": "ur/train-*"}, {"split": "test", "path": "ur/test-*"}, {"split": "validation", "path": "ur/validation-*"}]}, {"config_name": "vi", "data_files": [{"split": "train", "path": "vi/train-*"}, {"split": "test", "path": "vi/test-*"}, {"split": "validation", "path": "vi/validation-*"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "zh/train-*"}, {"split": "test", "path": "zh/test-*"}, {"split": "validation", "path": "zh/validation-*"}]}]}
TAGS
#language-Arabic #language-Bulgarian #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-French #language-Hindi #language-Russian #language-Swahili (macrolanguage) #language-Thai #language-Turkish #language-Urdu #language-Vietnamese #language-Chinese #region-us 

Dataset Card for "xnli"
=======================


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: 7.74 GB
* Size of the generated dataset: 3.23 GB
* Total amount of disk used: 10.97 GB


### Dataset Summary


XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).


### Supported Tasks and Leaderboards


### Languages


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


### Data Instances


#### all\_languages


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 1.61 GB
* Total amount of disk used: 2.09 GB


An example of 'train' looks as follows.


#### ar


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 109.32 MB
* Total amount of disk used: 593.29 MB


An example of 'validation' looks as follows.


#### bg


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 128.32 MB
* Total amount of disk used: 612.28 MB


An example of 'train' looks as follows.


#### de


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 86.17 MB
* Total amount of disk used: 570.14 MB


An example of 'train' looks as follows.


#### el


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 142.30 MB
* Total amount of disk used: 626.26 MB


An example of 'validation' looks as follows.


### Data Fields


The data fields are the same among all splits.


#### all\_languages


* 'premise': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'.
* 'hypothesis': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).


#### ar


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).


#### bg


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).


#### de


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).


#### el


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2).


### 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, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.



### Dataset Summary


XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels). ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### all\_languages


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 1.61 GB
* Total amount of disk used: 2.09 GB


An example of 'train' looks as follows. #### ar


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 109.32 MB
* Total amount of disk used: 593.29 MB


An example of 'validation' looks as follows. #### bg


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 128.32 MB
* Total amount of disk used: 612.28 MB


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


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 86.17 MB
* Total amount of disk used: 570.14 MB


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


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 142.30 MB
* Total amount of disk used: 626.26 MB


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


The data fields are the same among all splits. #### all\_languages


* 'premise': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'.
* 'hypothesis': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### ar


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### bg


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### de


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### el


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). ### 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, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.
TAGS
#language-Arabic #language-Bulgarian #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-French #language-Hindi #language-Russian #language-Swahili (macrolanguage) #language-Thai #language-Turkish #language-Urdu #language-Vietnamese #language-Chinese #region-us 
 ### Dataset Summary


XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels). ### Supported Tasks and Leaderboards ### Languages


Dataset Structure
----------------- ### Data Instances #### all\_languages


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 1.61 GB
* Total amount of disk used: 2.09 GB


An example of 'train' looks as follows. #### ar


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 109.32 MB
* Total amount of disk used: 593.29 MB


An example of 'validation' looks as follows. #### bg


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 128.32 MB
* Total amount of disk used: 612.28 MB


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


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 86.17 MB
* Total amount of disk used: 570.14 MB


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


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 142.30 MB
* Total amount of disk used: 626.26 MB


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


The data fields are the same among all splits. #### all\_languages


* 'premise': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'.
* 'hypothesis': a multilingual 'string' variable, with possible languages including 'ar', 'bg', 'de', 'el', 'en'.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### ar


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### bg


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### de


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). #### el


* 'premise': a 'string' feature.
* 'hypothesis': a 'string' feature.
* 'label': a classification label, with possible values including 'entailment' (0), 'neutral' (1), 'contradiction' (2). ### 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, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.
passage: TAGS
#language-Arabic #language-Bulgarian #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-French #language-Hindi #language-Russian #language-Swahili (macrolanguage) #language-Thai #language-Turkish #language-Urdu #language-Vietnamese #language-Chinese #region-us 
### Dataset Summary


XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).### Supported Tasks and Leaderboards### Languages


Dataset Structure
-----------------### Data Instances#### all\_languages


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 1.61 GB
* Total amount of disk used: 2.09 GB


An example of 'train' looks as follows.#### ar


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 109.32 MB
* Total amount of disk used: 593.29 MB


An example of 'validation' looks as follows.#### bg


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 128.32 MB
* Total amount of disk used: 612.28 MB


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


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 86.17 MB
* Total amount of disk used: 570.14 MB


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


* Size of downloaded dataset files: 483.96 MB
* Size of the generated dataset: 142.30 MB
* Total amount of disk used: 626.26 MB


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


The data fields are the same among all splits.