# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

This repository contains a machine-translated French version of the portion of [MultiNLI](https://cims.nyu.edu/~sbowman/multinli) concerning the 9/11 terrorist attacks (2000 examples).
Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.

In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples.

### Supported Tasks and Leaderboards

This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.

## Dataset Structure

### Data Fields

- `premise`: The machine translated premise in the target language.
- `hypothesis`: The machine translated premise in the target language.
- `label`: The classification label, with possible values 0 (`entailment`), 1 (`neutral`), 2 (`contradiction`).
- `label_text`: The classification label, with possible values `entailment` (0), `neutral` (1), `contradiction` (2).
- `pairID`: Unique identifier for pair.
- `promptID`: Unique identifier for prompt.
- `premise_original`: The original premise from the English source dataset.
- `hypothesis_original`: The original hypothesis from the English source dataset.

### Data Splits

|  name  |entailment|neutral|contradiction|
|--------|---------:|------:|------------:|
|mnli_fr |   705    |  641  |     654     |

## Dataset Creation

The dataset was machine translated from English to French using the latest neural machine translation [opus-mt-tc-big](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-fr) model available for French. 
The translation of the sentences was carried out on March 29th, 2023.

## Additional Information

### Citation Information

**BibTeX:**

````BibTeX
@InProceedings{N18-1101,
  author = "Williams, Adina
            and Nangia, Nikita
            and Bowman, Samuel",
  title = "A Broad-Coverage Challenge Corpus for
           Sentence Understanding through Inference",
  booktitle = "Proceedings of the 2018 Conference of
               the North American Chapter of the
               Association for Computational Linguistics:
               Human Language Technologies, Volume 1 (Long
               Papers)",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  pages = "1112--1122",
  location = "New Orleans, Louisiana",
  url = "http://aclweb.org/anthology/N18-1101"
}
````

**ACL:**

Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://aclanthology.org/N18-1101/). In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.

### Acknowledgements

This translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France.
task_categories:text-classification task_ids:natural-language-inference task_ids:multi-input-text-classification size_categories:1K<n<10K language:fr license:bsd-2-clause region:us
{"language": ["fr"], "license": "bsd-2-clause", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us 

Dataset Card for Dataset Name
=============================


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


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


### Dataset Summary


This repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples).
Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.


In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples.


### Supported Tasks and Leaderboards


This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.


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


### Data Fields


* 'premise': The machine translated premise in the target language.
* 'hypothesis': The machine translated premise in the target language.
* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').
* 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'pairID': Unique identifier for pair.
* 'promptID': Unique identifier for prompt.
* 'premise\_original': The original premise from the English source dataset.
* 'hypothesis\_original': The original hypothesis from the English source dataset.


### Data Splits



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


The dataset was machine translated from English to French using the latest neural machine translation opus-mt-tc-big model available for French.
The translation of the sentences was carried out on March 29th, 2023.


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


BibTeX:


'


ACL:


Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.


### Acknowledgements


This translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.



### Dataset Summary


This repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples).
Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.


In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples. ### Supported Tasks and Leaderboards


This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.


Dataset Structure
----------------- ### Data Fields


* 'premise': The machine translated premise in the target language.
* 'hypothesis': The machine translated premise in the target language.
* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').
* 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'pairID': Unique identifier for pair.
* 'promptID': Unique identifier for prompt.
* 'premise\_original': The original premise from the English source dataset.
* 'hypothesis\_original': The original hypothesis from the English source dataset. ### Data Splits



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


The dataset was machine translated from English to French using the latest neural machine translation opus-mt-tc-big model available for French.
The translation of the sentences was carried out on March 29th, 2023.


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


BibTeX:


'


ACL:


Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics. ### Acknowledgements


This translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #task_ids-multi-input-text-classification #size_categories-1K<n<10K #language-French #license-bsd-2-clause #region-us 
 ### Dataset Summary


This repository contains a machine-translated French version of the portion of MultiNLI concerning the 9/11 terrorist attacks (2000 examples).
Note that these 2000 examples included in MultiNLI (and machine translated in French here) on the subject of 9/11 are different from the 249 examples in the validation subset and the 501 ones in the test subset of XNLI on the same subject.


In the original subset of MultiNLI on 9/11, 26 examples were left without gold label. In this French version, we have given a gold label also to these examples (so that there are no more examples without gold label), according to our reading of the examples. ### Supported Tasks and Leaderboards


This dataset can be used for the task of Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), which is a sentence-pair classification task.


Dataset Structure
----------------- ### Data Fields


* 'premise': The machine translated premise in the target language.
* 'hypothesis': The machine translated premise in the target language.
* 'label': The classification label, with possible values 0 ('entailment'), 1 ('neutral'), 2 ('contradiction').
* 'label\_text': The classification label, with possible values 'entailment' (0), 'neutral' (1), 'contradiction' (2).
* 'pairID': Unique identifier for pair.
* 'promptID': Unique identifier for prompt.
* 'premise\_original': The original premise from the English source dataset.
* 'hypothesis\_original': The original hypothesis from the English source dataset. ### Data Splits



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


The dataset was machine translated from English to French using the latest neural machine translation opus-mt-tc-big model available for French.
The translation of the sentences was carried out on March 29th, 2023.


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


BibTeX:


'


ACL:


Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In *Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics. ### Acknowledgements


This translation of the original dataset was done as part of a research project supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, *Institut Cybersécurité Occitanie*, funded by Région Occitanie, France.