
## Corpus Summary

This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.
The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in [Text2FaceGAN](https://arxiv.org/pdf/1911.11378.pdf). 
In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.
Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA 
dataset

The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model [Sent2vec-CelebA-Sp](https://huggingface.co/oeg/Sent2vec_CelebA_Sp).

## Corpus Fields
Each corpus entry is composed of:
- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.

You can download the file with a _.txt_ or _.csv_ extension as appropriate. 

## Citation information
**Citing**: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in **[Information Processing and Management](https://doi.org/10.1016/j.ipm.2024.103667)**:

```bib
@article{YAURILOZANO2024103667,
title = {Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish},
journal = {Information Processing & Management},
volume = {61},
number = {3},
pages = {103667},
year = {2024},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2024.103667},
url = {https://www.sciencedirect.com/science/article/pii/S030645732400027X},
author = {Eduardo Yauri-Lozano and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro}
}
```

## License

This corpus is available under the **[Apache License 2.0](https://github.com/manwestc/TINTO/blob/main/LICENSE)**.

## Autors
- [Eduardo Yauri Lozano](https://github.com/eduar03yauri)
- [Manuel Castillo-Cara](https://github.com/manwestc)
- [Raúl García-Castro](https://github.com/rgcmme)

[*Universidad Nacional de Ingeniería*](https://www.uni.edu.pe/), [*Ontology Engineering Group*](https://oeg.fi.upm.es/), [*Universidad Politécnica de Madrid.*](https://www.upm.es/internacional)

## Contributors
See the full list of contributors [here](https://github.com/eduar03yauri/DCGAN-text2face-forSpanish).

<kbd><img src="https://www.uni.edu.pe/images/logos/logo_uni_2016.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-oeg.png" alt="Ontology Engineering Group" width="100"></kbd> 
<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-upm.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>



task_categories:table-question-answering task_categories:question-answering task_categories:translation task_categories:text2text-generation size_categories:100M<n<1B language:es license:apache-2.0 CelebA Spanish celebFaces attributes face detection face recognition arxiv:1911.11378 doi:10.57967/hf/0446 region:us
{"language": ["es"], "license": "apache-2.0", "size_categories": ["100M<n<1B"], "task_categories": ["table-question-answering", "question-answering", "translation", "text2text-generation"], "pretty_name": "Sent2vec+CelebA training corpus in Spanish", "tags": ["CelebA", "Spanish", "celebFaces attributes", "face detection", "face recognition"]}
TAGS
#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0446 #region-us 


## Corpus Summary

This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.
The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. 
In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.
Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA 
dataset

The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp.

## Corpus Fields
Each corpus entry is composed of:
- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.

You can download the file with a _.txt_ or _.csv_ extension as appropriate. 

information
Citing: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management:



## License

This corpus is available under the Apache License 2.0.

## Autors
- Eduardo Yauri Lozano
- Manuel Castillo-Cara
- Raúl García-Castro

*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*

## Contributors
See the full list of contributors here.

<kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
<kbd><img src="URL alt="Ontology Engineering Group" width="100"></kbd> 
<kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>



## Corpus Summary

This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.
The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. 
In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.
Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA 
dataset

The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp. ## Corpus Fields
Each corpus entry is composed of:
- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.

You can download the file with a _.txt_ or _.csv_ extension as appropriate. 

information
Citing: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management: ## License

This corpus is available under the Apache License 2.0. ## Autors
- Eduardo Yauri Lozano
- Manuel Castillo-Cara
- Raúl García-Castro

*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.* ## Contributors
See the full list of contributors here.

<kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
<kbd><img src="URL alt="Ontology Engineering Group" width="100"></kbd> 
<kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
TAGS
#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0446 #region-us 
 ## Corpus Summary

This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.
The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. 
In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.
Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA 
dataset

The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp. ## Corpus Fields
Each corpus entry is composed of:
- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.

You can download the file with a _.txt_ or _.csv_ extension as appropriate. 

information
Citing: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management: ## License

This corpus is available under the Apache License 2.0. ## Autors
- Eduardo Yauri Lozano
- Manuel Castillo-Cara
- Raúl García-Castro

*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.* ## Contributors
See the full list of contributors here.

<kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
<kbd><img src="URL alt="Ontology Engineering Group" width="100"></kbd> 
<kbd><img src="URL alt="Universidad Politécnica de Madrid" width="100"></kbd>
passage: TAGS
#task_categories-table-question-answering #task_categories-question-answering #task_categories-translation #task_categories-text2text-generation #size_categories-100M<n<1B #language-Spanish #license-apache-2.0 #CelebA #Spanish #celebFaces attributes #face detection #face recognition #arxiv-1911.11378 #doi-10.57967/hf/0446 #region-us 
## Corpus Summary

This corpus has 192050 entries made up of descriptive sentences of the faces of the CelebA dataset.
The preprocessing of the corpus has been to translate into Spanish the captions of the CelebA dataset with the algorithm used in Text2FaceGAN. 
In particular, all sentences are combined to generate a larger corpus. Additionally, a data preprocessing was applied that consists of eliminating stopwords, separation symbols and complementary elements that are not useful for training.
Finally, using the Sent2vec library and the corpus, training was done to obtain an encoder model for sentences in the Spanish language. Specifically for captions from the CelebA 
dataset

The training of Sent2vec + CelebA, using the present corpus was developed, resulting in the new model Sent2vec-CelebA-Sp.## Corpus Fields
Each corpus entry is composed of:
- Descriptive sentence of a face from the CelebA dataset applied the corresponding preprocessing.

You can download the file with a _.txt_ or _.csv_ extension as appropriate. 

information
Citing: If you used CelebA_Sent2vec_Sp corpus in your work, please cite the paper publish in Information Processing and Management:## License

This corpus is available under the Apache License 2.0.## Autors
- Eduardo Yauri Lozano
- Manuel Castillo-Cara
- Raúl García-Castro

*Universidad Nacional de Ingeniería*, *Ontology Engineering Group*, *Universidad Politécnica de Madrid.*