
# stsb_multi_mt_fr_prompt_sentence_similarity
## Summary

**stsb_multi_mt_fr_prompt_sentence_similarity** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).  
It contains **155,304** rows that can be used for a semantic similarity scoring task.  
The original data (without prompts) comes from the dataset [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt) by May 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
18 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.

```
'Déterminer le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Déterminez le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Détermine le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Indiquer le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Indiquez le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Indique le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Donner le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Donnez le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Donne le score de similarité entre les deux phrases suivantes. Phrase 1 : "'+sentence1+'"\n Phrase 2 : "'+sentence2+'"',  
'Déterminer le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Déterminez le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Détermine le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Indiquer le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Indiquez le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Indique le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Donner le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Donnez le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
'Donne le score de similarité entre la phrase : "'+sentence1+'"\n et la phrase : "'+sentence2+'"\n Similarité : ',  
```

### Features used in the prompts
In the prompt list above, `sentence1`, `sentence2` and the `target` have been constructed from:
```
stsb = load_dataset('stsb_multi_mt','fr')  
sentence1 = stsb['train'][i]['sentence1']  
sentence2 = stsb['train'][i]['sentence2']  
targets = float(stsb['train'][i]['similarity_score'])/5.0)
```


# Splits
- `train` with 103,482 samples
- `valid` with 27,000 samples
- `test` with 24,822 samples


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

# Citation
## Original data
> @InProceedings{huggingface:dataset:stsb_multi_mt,  
title = {Machine translated multilingual STS benchmark dataset.},  
author={Philip May},  
year={2021},  
url={https://github.com/PhilipMay/stsb-multi-mt}  
}

## 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
https://github.com/PhilipMay/stsb-multi-mt/blob/main/LICENSE
task_categories:text-classification task_ids:text-scoring task_ids:semantic-similarity-scoring annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100k<n<1M source_datasets:stsb_multi_mt language:fr license:other DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["100k<n<1M"], "source_datasets": ["stsb_multi_mt"], "task_categories": ["text-classification"], "task_ids": ["text-scoring", "semantic-similarity-scoring"], "tags": ["DFP", "french prompts"]}
TAGS
#task_categories-text-classification #task_ids-text-scoring #task_ids-semantic-similarity-scoring #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100k<n<1M #source_datasets-stsb_multi_mt #language-French #license-other #DFP #french prompts #region-us 


# stsb_multi_mt_fr_prompt_sentence_similarity
## Summary

stsb_multi_mt_fr_prompt_sentence_similarity is a subset of the Dataset of French Prompts (DFP).  
It contains 155,304 rows that can be used for a semantic similarity scoring task.  
The original data (without prompts) comes from the dataset stsb_multi_mt by May 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
18 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, 'sentence1', 'sentence2' and the 'target' have been constructed from:



# Splits
- 'train' with 103,482 samples
- 'valid' with 27,000 samples
- 'test' with 24,822 samples


# How to use?


## Original data
> @InProceedings{huggingface:dataset:stsb_multi_mt,  
title = {Machine translated multilingual STS benchmark dataset.},  
author={Philip May},  
year={2021},  
url={URL  
}

## 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
URL
# stsb_multi_mt_fr_prompt_sentence_similarity ## Summary

stsb_multi_mt_fr_prompt_sentence_similarity is a subset of the Dataset of French Prompts (DFP).  
It contains 155,304 rows that can be used for a semantic similarity scoring task.  
The original data (without prompts) comes from the dataset stsb_multi_mt by May 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
18 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, 'sentence1', 'sentence2' and the 'target' have been constructed from: # Splits
- 'train' with 103,482 samples
- 'valid' with 27,000 samples
- 'test' with 24,822 samples # How to use? ## Original data
> @InProceedings{huggingface:dataset:stsb_multi_mt,  
title = {Machine translated multilingual STS benchmark dataset.},  
author={Philip May},  
year={2021},  
url={URL  
} ## 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
URL
TAGS
#task_categories-text-classification #task_ids-text-scoring #task_ids-semantic-similarity-scoring #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100k<n<1M #source_datasets-stsb_multi_mt #language-French #license-other #DFP #french prompts #region-us 
 # stsb_multi_mt_fr_prompt_sentence_similarity ## Summary

stsb_multi_mt_fr_prompt_sentence_similarity is a subset of the Dataset of French Prompts (DFP).  
It contains 155,304 rows that can be used for a semantic similarity scoring task.  
The original data (without prompts) comes from the dataset stsb_multi_mt by May 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
18 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, 'sentence1', 'sentence2' and the 'target' have been constructed from: # Splits
- 'train' with 103,482 samples
- 'valid' with 27,000 samples
- 'test' with 24,822 samples # How to use? ## Original data
> @InProceedings{huggingface:dataset:stsb_multi_mt,  
title = {Machine translated multilingual STS benchmark dataset.},  
author={Philip May},  
year={2021},  
url={URL  
} ## 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
URL