
# bisect_fr_prompt_textual_merging
## Summary

**bisect_fr_prompt_textual_merging** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **10,383,891** rows that can be used for a textual fusion 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
21 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.

```
'Fusionner les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version fusionnée : ',  
'Fusionne les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version fusionnée : ',  
'Fusionnez les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version fusionnée : ',  
'Combiner les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version combinée : ',  
'Combine les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version combinée : ',  
'Combinez les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version combinée : ',  
'Réunir les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version réunie : ',  
'Réunis les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version réunie : ',  
'Réunissez les deux phrases suivantes en une seule tout en conservant leurs sens : "'+source+'" Version réunie : ',  
'"'+source+' Fournir une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Fournis une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Fournissez une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Ecrire une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Ecris une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Ecrivez une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Rédiger une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Rédige une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Rédigez une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Générer une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Génère une version synonyme en une phrase des deux phrases précédentes : ',  
'"'+source+' Générez une version synonyme en une phrase des deux phrases précédentes : '
```

### 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]['target'].replace(' . ','. ').replace(' .','. ').replace(' , ',', ').replace(', ',', ').replace(' _SPLIT_','')[:-1]
targets = bisect['train'][i]['source'].replace(' . ','. ').replace(' .','. ').replace(' , ',', ').replace(', ',', ').replace('_SPLIT_','')[:-1]
```


# Splits
- `train` with 10,311,735 samples
- `valid` with 50,400 samples
- `test` with 21,756 samples


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

# 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:10M<n<100M source_datasets:bisect language:fr license:cc-by-nc-4.0 textual-fusion DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["bisect"], "task_categories": ["summarization"], "tags": ["textual-fusion", "DFP", "french prompts"]}
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-bisect #language-French #license-cc-by-nc-4.0 #textual-fusion #DFP #french prompts #region-us 


# bisect_fr_prompt_textual_merging
## Summary

bisect_fr_prompt_textual_merging is a subset of the Dataset of French Prompts (DFP).  
It contains 10,383,891 rows that can be used for a textual fusion 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
21 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 10,311,735 samples
- 'valid' with 50,400 samples
- 'test' with 21,756 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_merging ## Summary

bisect_fr_prompt_textual_merging is a subset of the Dataset of French Prompts (DFP).  
It contains 10,383,891 rows that can be used for a textual fusion 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
21 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 10,311,735 samples
- 'valid' with 50,400 samples
- 'test' with 21,756 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-10M<n<100M #source_datasets-bisect #language-French #license-cc-by-nc-4.0 #textual-fusion #DFP #french prompts #region-us 
 # bisect_fr_prompt_textual_merging ## Summary

bisect_fr_prompt_textual_merging is a subset of the Dataset of French Prompts (DFP).  
It contains 10,383,891 rows that can be used for a textual fusion 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
21 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 10,311,735 samples
- 'valid' with 50,400 samples
- 'test' with 21,756 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-10M<n<100M #source_datasets-bisect #language-French #license-cc-by-nc-4.0 #textual-fusion #DFP #french prompts #region-us 
# bisect_fr_prompt_textual_merging## Summary

bisect_fr_prompt_textual_merging is a subset of the Dataset of French Prompts (DFP).  
It contains 10,383,891 rows that can be used for a textual fusion 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
21 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 10,311,735 samples
- 'valid' with 50,400 samples
- 'test' with 21,756 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}
}