
# french_book_reviews_fr_prompt_stars_classification
## Summary

**french_book_reviews_fr_prompt_stars_classification** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **270,424** rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset [french_book_reviews](https://huggingface.co/datasets/Abirate/french_book_reviews) by Eltaief.  
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.

```
"""Donner un nombre d'étoiles à l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donne un nombre d'étoiles à l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donnez un nombre d'étoiles à l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donner un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donne un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donnez un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donner un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donne un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Donnez un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Noter avec un nombre d'étoiles l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Note avec un nombre d'étoiles l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Notez avec un nombre d'étoiles l'avis ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Noter avec un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Note avec un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Notez avec un nombre d'étoiles le commentaire ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Noter avec un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Note avec un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
"""Notez avec un nombre d'étoiles la critique ci-dessous (1 étant le plus bas et 5 le plus haut) : """+review,
review+'Pour ce texte, je donne la note de ',
'Texte : '+review+'\n Étoiles :',
'Texte : '+review+'\n Note (entre 1 et 5) :',
'Commentaire : '+review+'\n Sur une échelle de 1 à 5, je donnerais une note de :'
```

### Features used in the prompts
In the prompt list above, `review` and `targets` have been constructed from:
```
fbr = load_dataset('Abirate/french_book_reviews')
review = fbr['train']['reader_review'][i]
targets = fbr['train']['rating'][i]
```



# Splits
- `train` with 270,424 samples
- no `valid` split
- no `test` split


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

# Citation
## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { https://huggingface.co/datasets/Abirate/french_book_reviews },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }}


## 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
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
task_categories:text-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:french_book_reviews language:fr license:cc stars-classification DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["french_book_reviews"], "task_categories": ["text-classification"], "tags": ["stars-classification", "DFP", "french prompts"]}
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-french_book_reviews #language-French #license-cc #stars-classification #DFP #french prompts #region-us 


# french_book_reviews_fr_prompt_stars_classification
## Summary

french_book_reviews_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
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, 'review' and 'targets' have been constructed from:




# Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split


# How to use?


## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }}


## 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
CC0: Public Domain
# french_book_reviews_fr_prompt_stars_classification ## Summary

french_book_reviews_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
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, 'review' and 'targets' have been constructed from: # Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }} ## 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
CC0: Public Domain
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-french_book_reviews #language-French #license-cc #stars-classification #DFP #french prompts #region-us 
 # french_book_reviews_fr_prompt_stars_classification ## Summary

french_book_reviews_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 270,424 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset french_book_reviews by Eltaief.  
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, 'review' and 'targets' have been constructed from: # Splits
- 'train' with 270,424 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> @misc {abir_eltaief_2023,  
	author       = { {Abir ELTAIEF} },  
	title        = { french_book_reviews (Revision 534725e) },  
	year         = 2023,  
	url          = { URL },  
	doi          = { 10.57967/hf/1052 },  
	publisher    = { Hugging Face }} ## 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
CC0: Public Domain