
# mnli_fr_prompt_textual_entailment
## Summary

**mnli_fr_prompt_textual_entailment** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **550,000** rows that can be used for a textual entailment task.  
The original data (without prompts) comes from the dataset [multilingual-NLI-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-NLI-26lang-2mil7) by Laurer et al. where only the mnli French part has been kept.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.


## Prompts used
### List
22 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.

```
"""Prendre l'énoncé suivant comme vrai : "  """+premise+"""  "\n Alors l'énoncé suivant : "  """+hypothesis+"""  " est "vrai", "faux", ou "incertain" ?""", 
"""Prends l'énoncé suivant comme vrai : "  """+premise+"""  "\n Alors l'énoncé suivant : "  """+hypothesis+"""  " est "vrai", "faux", ou "incertain" ?""",   
"""Prenez l'énoncé suivant comme vrai : "  """+premise+"""  "\n Alors l'énoncé suivant : "  """+hypothesis+"""  " est "vrai", "faux", ou "incertain" ?""",   
'"'+premise+'"\nQuestion : Cela implique-t-il que "'+hypothesis+'" ? "vrai", "faux", ou "incertain" ?',   
'"'+premise+'"\nQuestion : "'+hypothesis+'" est "vrai", "faux", ou "peut-être" ?',   
""" "  """+premise+"""  "\n D'après le passage précédent, est-il vrai que "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
""" "  """+premise+"""  "\nSur la base de ces informations, l'énoncé est-il : "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
""" "  """+premise+"""  "\nEn gardant à l'esprit le texte ci-dessus, considérez : "  """+hypothesis+"""  "\n Est-ce que c'est "vrai", "faux", ou "incertain" ?""",   
""" "  """+premise+"""  "\nEn gardant à l'esprit le texte ci-dessus, considére : "  """+hypothesis+"""  "\n Est-ce que c'est "vrai", "faux", ou "peut-être" ?""",   
""" "  """+premise+"""  "\nEn utilisant uniquement la description ci-dessus et ce que vous savez du monde, "  """+hypothesis+"""  " est-ce "vrai", "faux", ou "incertain" ?""",   
""" "  """+premise+"""  "\nEn utilisant uniquement la description ci-dessus et ce que tu sais du monde, "  """+hypothesis+"""  " est-ce "vrai", "faux", ou "incertain" ?""",   
"""Étant donné que "  """+premise+"""  ", s'ensuit-il que "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
"""Étant donné que "  """+premise+"""  ", est-il garanti que "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
'Étant donné '+premise+', doit-on supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',   
'Étant donné '+premise+', dois-je supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',   
'Sachant que '+premise+', doit-on supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',   
'Sachant que '+premise+', dois-je supposer que '+hypothesis+' est "vrai", "faux", ou "incertain" ?',   
'Étant donné que '+premise+', il doit donc être vrai que '+hypothesis+' ? "vrai", "faux", ou "incertain" ?',   
"""Supposons que "  """+premise+"""  ", pouvons-nous déduire que "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
"""Supposons que "  """+premise+"""  ", puis-je déduire que "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
"""Supposons qu'il est vrai que "  """+premise+"""  ". Alors, est-ce que "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?""",   
"""Supposons qu'il soit vrai que "  """+premise+"""  ",\n Donc, "  """+hypothesis+"""  " ? "vrai", "faux", ou "incertain" ?"""
```

### Features used in the prompts
In the prompt list above, `premise`, `hypothesis` and `targets` have been constructed from:
```
moritz = load_dataset('MoritzLaurer/multilingual-NLI-26lang-2mil7')
mnli = moritz['fr_mnli']
mnli['premise'] = list(map(lambda i: i.replace(' . ','. ').replace(' .','. ').replace('( ','(').replace(' )',')').replace(' , ',', ').replace(', ',', ').replace("' ","'"), map(str,mnli['premise'])))
mnli['hypothesis'] = list(map(lambda x: x.replace(' . ','. ').replace(' .','. ').replace('( ','(').replace(' )',')').replace(' , ',', ').replace(', ',', ').replace("' ","'"), map(str,mnli['hypothesis'])))
targets = str(anli['label'][i]).replace("0","vrai").replace("1","incertain").replace("2","faux")
```


# Splits
- `train` with 550,000 samples
- no `valid` split
- no `test` split


# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/mnli_fr_prompt_textual_entailment")
```

# Citation
## Original data
> @article{laurer_less_2022,
    title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}},
    url = {https://osf.io/74b8k},
    language = {en-us},
    urldate = {2022-07-28},
    journal = {Preprint},
    author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
    month = jun,
    year = {2022},
    note = {Publisher: Open Science Framework},
}


## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { https://huggingface.co/datasets/CATIE-AQ/DFP },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
}


## License 
mit
task_categories:text-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:multilingual-NLI-26lang-2mil7 language:fr textual-entailment DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["multilingual-NLI-26lang-2mil7"], "task_categories": ["text-classification"], "licence": "mit", "tags": ["textual-entailment", "DFP", "french prompts"]}
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-multilingual-NLI-26lang-2mil7 #language-French #textual-entailment #DFP #french prompts #region-us 


# mnli_fr_prompt_textual_entailment
## Summary

mnli_fr_prompt_textual_entailment is a subset of the Dataset of French Prompts (DFP).  
It contains 550,000 rows that can be used for a textual entailment task.  
The original data (without prompts) comes from the dataset multilingual-NLI-26lang-2mil7 by Laurer et al. where only the mnli French part has been kept.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al.


## Prompts used
### List
22 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.



### Features used in the prompts
In the prompt list above, 'premise', 'hypothesis' and 'targets' have been constructed from:



# Splits
- 'train' with 550,000 samples
- no 'valid' split
- no 'test' split


# How to use?


## Original data
> @article{laurer_less_2022,
    title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}},
    url = {URL
    language = {en-us},
    urldate = {2022-07-28},
    journal = {Preprint},
    author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
    month = jun,
    year = {2022},
    note = {Publisher: Open Science Framework},
}


## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
}


## License 
mit
# mnli_fr_prompt_textual_entailment ## Summary

mnli_fr_prompt_textual_entailment is a subset of the Dataset of French Prompts (DFP).  
It contains 550,000 rows that can be used for a textual entailment task.  
The original data (without prompts) comes from the dataset multilingual-NLI-26lang-2mil7 by Laurer et al. where only the mnli French part has been kept.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al. ## Prompts used ### List
22 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ### Features used in the prompts
In the prompt list above, 'premise', 'hypothesis' and 'targets' have been constructed from: # Splits
- 'train' with 550,000 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> @article{laurer_less_2022,
    title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}},
    url = {URL
    language = {en-us},
    urldate = {2022-07-28},
    journal = {Preprint},
    author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
    month = jun,
    year = {2022},
    note = {Publisher: Open Science Framework},
} ## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
} ## License 
mit
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-multilingual-NLI-26lang-2mil7 #language-French #textual-entailment #DFP #french prompts #region-us 
 # mnli_fr_prompt_textual_entailment ## Summary

mnli_fr_prompt_textual_entailment is a subset of the Dataset of French Prompts (DFP).  
It contains 550,000 rows that can be used for a textual entailment task.  
The original data (without prompts) comes from the dataset multilingual-NLI-26lang-2mil7 by Laurer et al. where only the mnli French part has been kept.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al. ## Prompts used ### List
22 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ### Features used in the prompts
In the prompt list above, 'premise', 'hypothesis' and 'targets' have been constructed from: # Splits
- 'train' with 550,000 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> @article{laurer_less_2022,
    title = {Less {Annotating}, {More} {Classifying} – {Addressing} the {Data} {Scarcity} {Issue} of {Supervised} {Machine} {Learning} with {Deep} {Transfer} {Learning} and {BERT} - {NLI}},
    url = {URL
    language = {en-us},
    urldate = {2022-07-28},
    journal = {Preprint},
    author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
    month = jun,
    year = {2022},
    note = {Publisher: Open Science Framework},
} ## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,  
	author       = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { DFP (Revision 1d24c09) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1200 },  
	publisher    = { Hugging Face }  
} ## License 
mit
passage: TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-multilingual-NLI-26lang-2mil7 #language-French #textual-entailment #DFP #french prompts #region-us 
# mnli_fr_prompt_textual_entailment## Summary

mnli_fr_prompt_textual_entailment is a subset of the Dataset of French Prompts (DFP).  
It contains 550,000 rows that can be used for a textual entailment task.  
The original data (without prompts) comes from the dataset multilingual-NLI-26lang-2mil7 by Laurer et al. where only the mnli French part has been kept.  
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the xP3 dataset by Muennighoff et al.## Prompts used### List
22 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.### Features used in the prompts
In the prompt list above, 'premise', 'hypothesis' and 'targets' have been constructed from:# Splits
- 'train' with 550,000 samples
- no 'valid' split
- no 'test' split# How to use?