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- celebFaces Attributes
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# Sent2vec trained with data from the descriptive text corpus of the CelebA dataset
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## Overview
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- **Language**: Spanish
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- **Data**: [CelebA_Sent2vec_Sp](https://huggingface.co/datasets/oeg/CelebA_Sent2Vect_Sp).
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- **Architecture**: Sent2vec
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## Description
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## How to use
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## Licensing information
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This
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## Citation information
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## Autors
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- [Eduardo Yauri Lozano](https://github.com/eduar03yauri)
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- [Manuel Castillo-Cara](https://github.com/manwestc)
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[*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)
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## Contributors
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See the full list of contributors [here](https://github.com/eduar03yauri/DCGAN-text2face-forSpanishs).
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- celebFaces Attributes
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---
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# Sent2vec trained with data from the descriptive text corpus of the CelebA dataset
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## Overview
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- **Language**: Spanish
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- **Data**: [CelebA_Sent2vec_Sp](https://huggingface.co/datasets/oeg/CelebA_Sent2Vect_Sp).
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- **Architecture**: Sent2vec
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## Description
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Sent2vec can be used directly for English texts. However, since this work is used with Spanish text, it has been necessary to train it
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previously using the generated corpus ([in this respository](https://huggingface.co/datasets/oeg/CelebA_Sent2Vect_Sp)) with the following process:
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- Initial preprocessing of the Spanish corpus. For this purpose, a new file has been developed in which each of the entries of the original
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corpus is saved and the other components, such as the names of the image it describes and symbols, are removed.
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A total of 192,209 sentences are available for training.
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- Apply a second pre-processing consisting of removing accents. _stopwords_ and connectors were retained as part of
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- the sentence structure during training.
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- Configure the libraries, e.g., _Sent2vec_ and _FastText_, and the parameters. The parameters have been set empirically,
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being: 4,800 feature vector dimension, 5,000 epochs, 200 threads, 2 n-grams, and 0.05 learning rate.
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## How to use
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## Licensing information
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This model is available under the [Apache License 2.0.](https://www.apache.org/licenses/LICENSE-2.0)
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## Citation information
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**Citing**: If you used Sent2vec+CelebA model in your work, please cite the **[????](???)**:
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```bib
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@article{inffus_TINTO,
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title = {A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation},
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journal = {Information Fusion},
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author = {Reewos Talla-Chumpitaz and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro},
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volume = {91},
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pages = {173-186},
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year = {2023},
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issn = {1566-2535},
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doi = {https://doi.org/10.1016/j.inffus.2022.10.011}
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}
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```
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## Autors
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- [Eduardo Yauri Lozano](https://github.com/eduar03yauri)
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- [Manuel Castillo-Cara](https://github.com/manwestc)
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- [Raúl García-Castro](https://github.com/rgcmme)
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[*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)
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## Contributors
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See the full list of contributors [here](https://github.com/eduar03yauri/DCGAN-text2face-forSpanishs).
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<kbd><img src="https://www.uni.edu.pe/images/logos/logo_uni_2016.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
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<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-oeg.png" alt="Ontology Engineering Group" width="100"></kbd>
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<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-upm.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
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