
# bisect_fr_prompt_textual_simplification
## Summary

**bisect_fr_prompt_textual_simplification** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **9,889,420** rows that can be used for a textual simplification task.  
The original data (without prompts) comes from the dataset [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) by Kim et al. where only the 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
20 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.

```
'Simplifier la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',    
'Simplifie la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',    
'Simplifiez la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'Alléger la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'Allège la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'Allégez la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'Clarifier la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'Clarifie la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'Clarifiez la phrase suivante en la divisant tout en conservant son sens complet : "'+source+'" Version simplifiée : ',  
'"'+source+'" La phrase ci-dessus est trop compliquée. Fournir une version simplifiée composée de plusieurs phrases : ',  
'"'+source+'" La phrase ci-dessus est trop compliquée. Fournis une version simplifiée composée de plusieurs phrases : ',  
'"'+source+'" La phrase ci-dessus est trop compliquée. Fournissez une version simplifiée composée de plusieurs phrases : ',  
'"'+source+'" Cette phrase est difficile à comprendre. Une version plus simple avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est difficile à comprendre. Une version moins complexe avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est difficile à comprendre. Une version plus légère avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est difficile à comprendre. Une version épurée avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est lourde. Une version plus simple avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est lourde. Une version moins complexe avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est lourde. Une version plus légère avec une signification équivalente est la suivante : ',  
'"'+source+'" Cette phrase est lourde. Une version épurée avec une signification équivalente est la suivante : ' 
```

### Features used in the prompts
In the prompt list above, `source` and `targets` have been constructed from:
```
bisect = load_dataset('GEM/BiSECT','fr')
source = bisect['train'][i]['source'].replace(' . ','. ').replace(' .','. ').replace(' , ',', ').replace(', ',', ').replace('_SPLIT_','')[:-1]
targets = bisect['train'][i]['target'].replace(' . ','. ').replace(' .','. ').replace(' , ',', ').replace(', ',', ').replace(' _SPLIT_','')[:-1]
```


# Splits
- `train` with 9,820,700 samples
- `valid` with 48,000 samples
- `test` with 20,720 samples



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

# Citation
## Original data
> @inproceedings{bisect2021,
  title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts},
  author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris},
  booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2021}
}



## 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
cc-by-nc-4.0
task_categories:summarization annotations_creators:found language_creators:found multilinguality:monolingual size_categories:1M<n<10M source_datasets:bisect language:fr license:cc-by-nc-4.0 textual-simplification DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["bisect"], "task_categories": ["summarization"], "tags": ["textual-simplification", "DFP", "french prompts"]}
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-bisect #language-French #license-cc-by-nc-4.0 #textual-simplification #DFP #french prompts #region-us 


# bisect_fr_prompt_textual_simplification
## Summary

bisect_fr_prompt_textual_simplification is a subset of the Dataset of French Prompts (DFP).  
It contains 9,889,420 rows that can be used for a textual simplification task.  
The original data (without prompts) comes from the dataset BiSECT by Kim et al. where only the 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
20 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, 'source' and 'targets' have been constructed from:



# Splits
- 'train' with 9,820,700 samples
- 'valid' with 48,000 samples
- 'test' with 20,720 samples



# How to use?


## Original data
> @inproceedings{bisect2021,
  title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts},
  author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris},
  booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2021}
}



## 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
cc-by-nc-4.0
# bisect_fr_prompt_textual_simplification ## Summary

bisect_fr_prompt_textual_simplification is a subset of the Dataset of French Prompts (DFP).  
It contains 9,889,420 rows that can be used for a textual simplification task.  
The original data (without prompts) comes from the dataset BiSECT by Kim et al. where only the 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
20 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, 'source' and 'targets' have been constructed from: # Splits
- 'train' with 9,820,700 samples
- 'valid' with 48,000 samples
- 'test' with 20,720 samples # How to use? ## Original data
> @inproceedings{bisect2021,
  title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts},
  author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris},
  booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2021}
} ## 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
cc-by-nc-4.0
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-bisect #language-French #license-cc-by-nc-4.0 #textual-simplification #DFP #french prompts #region-us 
 # bisect_fr_prompt_textual_simplification ## Summary

bisect_fr_prompt_textual_simplification is a subset of the Dataset of French Prompts (DFP).  
It contains 9,889,420 rows that can be used for a textual simplification task.  
The original data (without prompts) comes from the dataset BiSECT by Kim et al. where only the 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
20 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, 'source' and 'targets' have been constructed from: # Splits
- 'train' with 9,820,700 samples
- 'valid' with 48,000 samples
- 'test' with 20,720 samples # How to use? ## Original data
> @inproceedings{bisect2021,
  title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts},
  author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris},
  booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2021}
} ## 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
cc-by-nc-4.0
passage: TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-bisect #language-French #license-cc-by-nc-4.0 #textual-simplification #DFP #french prompts #region-us 
# bisect_fr_prompt_textual_simplification## Summary

bisect_fr_prompt_textual_simplification is a subset of the Dataset of French Prompts (DFP).  
It contains 9,889,420 rows that can be used for a textual simplification task.  
The original data (without prompts) comes from the dataset BiSECT by Kim et al. where only the 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
20 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, 'source' and 'targets' have been constructed from:# Splits
- 'train' with 9,820,700 samples
- 'valid' with 48,000 samples
- 'test' with 20,720 samples# How to use?## Original data
> @inproceedings{bisect2021,
  title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts},
  author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris},
  booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year={2021}
}