

# amazon_reviews_multi_fr_prompt_stars_classification
## Summary

**amazon_reviews_multi_fr_prompt_stars_classification** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **4,620,000** rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) by Keung 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
28 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:
```
arm = load_dataset('amazon_reviews_multi', 'fr')
review = arm['train']['review_body'][i]
targets = arm['train']['stars'][i]

```


# Splits
- `train` with 4,400,000 samples
- `valid` with 110,000 samples
- `test` with 110,000 samples


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

# Citation
## Original data
> @inproceedings{marc_reviews,
    title={The Multilingual Amazon Reviews Corpus},
    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
    year={2020}
}


## 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
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc.  A copy of the agreement can be found at the dataset webpage here:
https://docs.opendata.aws/amazon-reviews-ml/license.txt

By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
task_categories:text-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:1M<n<10M source_datasets:amazon_reviews_multi language:fr license:other stars-classification DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["amazon_reviews_multi"], "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-1M<n<10M #source_datasets-amazon_reviews_multi #language-French #license-other #stars-classification #DFP #french prompts #region-us 



# amazon_reviews_multi_fr_prompt_stars_classification
## Summary

amazon_reviews_multi_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 4,620,000 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung 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
28 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 4,400,000 samples
- 'valid' with 110,000 samples
- 'test' with 110,000 samples


# How to use?


## Original data
> @inproceedings{marc_reviews,
    title={The Multilingual Amazon Reviews Corpus},
    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
    year={2020}
}


## 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
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc.  A copy of the agreement can be found at the dataset webpage here:
URL

By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the URL Conditions of Use and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
# amazon_reviews_multi_fr_prompt_stars_classification ## Summary

amazon_reviews_multi_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 4,620,000 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung 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
28 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 4,400,000 samples
- 'valid' with 110,000 samples
- 'test' with 110,000 samples # How to use? ## Original data
> @inproceedings{marc_reviews,
    title={The Multilingual Amazon Reviews Corpus},
    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
    year={2020}
} ## 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
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc.  A copy of the agreement can be found at the dataset webpage here:
URL

By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the URL Conditions of Use and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-amazon_reviews_multi #language-French #license-other #stars-classification #DFP #french prompts #region-us 
 # amazon_reviews_multi_fr_prompt_stars_classification ## Summary

amazon_reviews_multi_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 4,620,000 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung 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
28 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 4,400,000 samples
- 'valid' with 110,000 samples
- 'test' with 110,000 samples # How to use? ## Original data
> @inproceedings{marc_reviews,
    title={The Multilingual Amazon Reviews Corpus},
    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
    year={2020}
} ## 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
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc.  A copy of the agreement can be found at the dataset webpage here:
URL

By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the URL Conditions of Use and you agree to be bound by them, with the following additional conditions:

In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
passage: TAGS
#task_categories-text-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-amazon_reviews_multi #language-French #license-other #stars-classification #DFP #french prompts #region-us 
# amazon_reviews_multi_fr_prompt_stars_classification## Summary

amazon_reviews_multi_fr_prompt_stars_classification is a subset of the Dataset of French Prompts (DFP).  
It contains 4,620,000 rows that can be used for a stars-classification sentiment analysis task.  
The original data (without prompts) comes from the dataset amazon_reviews_multi by Keung 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
28 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 4,400,000 samples
- 'valid' with 110,000 samples
- 'test' with 110,000 samples# How to use?## Original data
> @inproceedings{marc_reviews,
    title={The Multilingual Amazon Reviews Corpus},
    author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
    year={2020}
}