
## Corpus Summary

This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the 
[sentence-transformer](https://www.sbert.net/) library to improve the efficiency of the [RoBERTa-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:

- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work [Text2faceGAN ](https://arxiv.org/pdf/1911.11378.pdf)
 
- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each
  pair of sentences. 
- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.
  Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.
- Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. 

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

## Corpus Fields
Each corpus entry is composed of:
- Sentence A: Descriptive sentence of a CelebA face in Spanish.
- Sentence B: Descriptive sentence of a CelebA face in Spanish.
- Similarity Value: Similarity of sentence A and sentence B.

Each component is separated by the character "|" with the structure:

```
SentenceA | Sentence B | similarity value
```
You can download the file with a _.txt_ or _.csv_ extension as appropriate.

## Citation information

**Citing**: If you used CelebA_RoBERTa_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/0447 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": "RoBERTa+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/0447 #region-us 


## Corpus Summary

This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the 
sentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:

- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN 
 
- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each
  pair of sentences. 
- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.
  Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.
- Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. 

The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp.

## Corpus Fields
Each corpus entry is composed of:
- Sentence A: Descriptive sentence of a CelebA face in Spanish.
- Sentence B: Descriptive sentence of a CelebA face in Spanish.
- Similarity Value: Similarity of sentence A and sentence B.

Each component is separated by the character "|" with the structure:


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

information

Citing: If you used CelebA_RoBERTa_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 contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the 
sentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:

- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN 
 
- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each
  pair of sentences. 
- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.
  Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.
- Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. 

The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp. ## Corpus Fields
Each corpus entry is composed of:
- Sentence A: Descriptive sentence of a CelebA face in Spanish.
- Sentence B: Descriptive sentence of a CelebA face in Spanish.
- Similarity Value: Similarity of sentence A and sentence B.

Each component is separated by the character "|" with the structure:


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

information

Citing: If you used CelebA_RoBERTa_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/0447 #region-us 
 ## Corpus Summary

This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the 
sentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:

- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN 
 
- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each
  pair of sentences. 
- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.
  Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.
- Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. 

The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp. ## Corpus Fields
Each corpus entry is composed of:
- Sentence A: Descriptive sentence of a CelebA face in Spanish.
- Sentence B: Descriptive sentence of a CelebA face in Spanish.
- Similarity Value: Similarity of sentence A and sentence B.

Each component is separated by the character "|" with the structure:


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

information

Citing: If you used CelebA_RoBERTa_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/0447 #region-us 
## Corpus Summary

This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the 
sentence-transformer library to improve the efficiency of the RoBERTa-large-bne base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:

- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work Text2faceGAN 
 
- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each
  pair of sentences. 
- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.
  Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.
- Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus. 

The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model RoBERTa-celebA-Sp.