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--- |
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license: apache-2.0 |
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datasets: |
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- oeg/CelebA_RoBERTa_Sp |
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language: |
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- es |
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tags: |
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- Spanish |
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- CelebA |
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- Roberta-base-bne |
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- celebFaces Attributes |
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pipeline_tag: text-to-image |
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--- |
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# RoBERTa base BNE 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_RoBERTa_Sp](https://huggingface.co/datasets/oeg/CelebA_RoBERTa_Sp). |
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- **Architecture**: roberta-base |
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## Description |
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In order to improve the RoBERTa encoder performance, this model has been trained using the generated corpus ([in this respository](https://huggingface.co/oeg/RoBERTa-CelebA-Sp/)) |
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and following the strategy of using a Siamese network together with the loss function of cosine similarity. The following steps were followed: |
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- Define sentence-transformer and torch libraries for the implementation of the encoder. |
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- Divide the training corpus into two parts, training with 249,999 sentences and validation with 10,000 sentences. |
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- Load training / validation data for the model. Two lists are generated for the storage of the information and, in each of them, |
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the entries are composed of a pair of descriptive sentences and their similarity value. |
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- Implement RoBERTa as a baseline model for transformer training. |
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- Train with a Siamese network in which, for a pair of sentences _A_ and _B_ from the training corpus, the similarities of their embedding |
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- vectors _u_ and _v_ generated using the cosine similarity metric (_CosineSimilarityLoss()_) are evaluated. |
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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 RoBERTa+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> |
