
# universal_dependencies_fr_fqb_fr_prompt_pos
## Summary

**universal_dependencies_fr_fqb_fr_prompt_pos** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).    
It contains **48,069** rows that can be used for a part-of-speech task.   
The original data (without prompts) comes from the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) where only the French fqb split by Seddah et Candito 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.

```
'Extraire les classes des mots du texte suivant : '+text,  
'Extrais les classes des mots du texte suivant : '+text,  
'Extrayez les classes des mots du texte suivant : '+text,  
'Isoler les classes des mots du texte suivant : '+text,  
'Isole les classes des mots du texte suivant : '+text,  
'Isolez les classes des mots du texte suivant : '+text,  
'Dégager les classes des mots dans le texte : '+text,  
'Dégage les classes des mots dans le texte : '+text,  
'Dégagez les classes des mots dans le texte : '+text,  
'Générer les classes des mots issues du texte suivant : '+text,  
'Génère les classes des mots issues du texte suivant : '+text,  
'Générez les classes des mots issues du texte suivant : '+text,  
'Trouver les classes des mots du texte : '+text,  
'Trouve les classes des mots du texte : '+text,  
'Trouvez les classes des mots du texte : '+text,  
'Repérer les classes des mots présentes dans le texte suivant : '+text,  
'Repère les classes des mots présentes dans le texte suivant : '+text,  
'Repérez les classes des mots présentes dans le texte suivant : '+text,  
'Indiquer les classes des mots du texte :'+text,  
'Indique les classes des mots du texte : '+text,  
'Indiquez les classes des mots du texte : '+text
```

### Features used in the prompts
In the prompt list above, `text` and `targets` have been constructed from:
```
fr_fqb = load_dataset('universal_dependencies', 'fr_fqb')  
# text
fr_fqb['test']['tokens'] = list(map(lambda i: ' '.join(fr_fqb['test']['tokens'][i]), range(len(fr_fqb['test']['tokens']))))
# targets
fr_fqb['test']['upos'] = list(map(lambda x: x.replace("[","").replace("]","").replace('17','AUX').replace('16','VERB').replace('15','INTJ').replace('14','ADV').replace('13','_').replace('12','X').replace('11','PRON').replace('10','PROPN').replace('9','CCONJ').replace('8','DET').replace('7','PART').replace('6','ADJ').replace('5','SCONJ').replace('4','SYM').replace('3','NUM').replace('2','ADP').replace('1','PUNCT').replace('0','NOUN'), map(str,fr_fqb['test']['upos'])))
```



# Splits
- `train` with 48,069 samples
- no `valid` split
- no `test` split


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

# Citation
## Original data
> Djamé Seddah, Marie Candito. Hard Time Parsing Questions: Building a QuestionBank for French. Tenth International Conference on Language Resources and Evaluation (LREC 2016), May 2016, Portorož, Slovenia.


## 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
LGPL-R
task_categories:token-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:universal_dependencies_fr_fqb language:fr license:lgpl pos DFP french prompts region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["fr"], "license": "lgpl", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["universal_dependencies_fr_fqb"], "task_categories": ["token-classification"], "tags": ["pos", "DFP", "french prompts"]}
TAGS
#task_categories-token-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-universal_dependencies_fr_fqb #language-French #license-lgpl #pos #DFP #french prompts #region-us 


# universal_dependencies_fr_fqb_fr_prompt_pos
## Summary

universal_dependencies_fr_fqb_fr_prompt_pos is a subset of the Dataset of French Prompts (DFP).    
It contains 48,069 rows that can be used for a part-of-speech task.   
The original data (without prompts) comes from the dataset universal_dependencies where only the French fqb split by Seddah et Candito 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, 'text' and 'targets' have been constructed from:




# Splits
- 'train' with 48,069 samples
- no 'valid' split
- no 'test' split


# How to use?


## Original data
> Djamé Seddah, Marie Candito. Hard Time Parsing Questions: Building a QuestionBank for French. Tenth International Conference on Language Resources and Evaluation (LREC 2016), May 2016, Portorož, Slovenia.


## 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
LGPL-R
# universal_dependencies_fr_fqb_fr_prompt_pos ## Summary

universal_dependencies_fr_fqb_fr_prompt_pos is a subset of the Dataset of French Prompts (DFP).    
It contains 48,069 rows that can be used for a part-of-speech task.   
The original data (without prompts) comes from the dataset universal_dependencies where only the French fqb split by Seddah et Candito 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, 'text' and 'targets' have been constructed from: # Splits
- 'train' with 48,069 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> Djamé Seddah, Marie Candito. Hard Time Parsing Questions: Building a QuestionBank for French. Tenth International Conference on Language Resources and Evaluation (LREC 2016), May 2016, Portorož, Slovenia. ## 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
LGPL-R
TAGS
#task_categories-token-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-universal_dependencies_fr_fqb #language-French #license-lgpl #pos #DFP #french prompts #region-us 
 # universal_dependencies_fr_fqb_fr_prompt_pos ## Summary

universal_dependencies_fr_fqb_fr_prompt_pos is a subset of the Dataset of French Prompts (DFP).    
It contains 48,069 rows that can be used for a part-of-speech task.   
The original data (without prompts) comes from the dataset universal_dependencies where only the French fqb split by Seddah et Candito 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, 'text' and 'targets' have been constructed from: # Splits
- 'train' with 48,069 samples
- no 'valid' split
- no 'test' split # How to use? ## Original data
> Djamé Seddah, Marie Candito. Hard Time Parsing Questions: Building a QuestionBank for French. Tenth International Conference on Language Resources and Evaluation (LREC 2016), May 2016, Portorož, Slovenia. ## 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
LGPL-R