Aitana
Collection
This collection features the Aitana family of generative language models developed by the GPLSI research group at Universidad de Alicante.
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21 items
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Updated
This model is fine-tuned from BSC-LT/mRoBERTa for binary classification of phishing detection in English texts.
It predicts whether a given SMS or email message belongs to the category of phishing or not phishing.
The dataset used for fine-tuning contains SMS and email texts labeled as phishing or not phishing.
Confusion Matrix
| Pred Not Phishing | Pred Phishing | |
|---|---|---|
| True Not Phishing | 1793 | 16 |
| True Phishing | 18 | 530 |
| Class | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| 0 (Not phishing) | 0.9901 | 0.9912 | 0.9906 | 1809 |
| 1 (Phishing) | 0.9707 | 0.9672 | 0.9689 | 548 |
Confusion Matrix
| Pred Not Phishing | Pred Phishing | |
|---|---|---|
| True Not Phishing | 823 | 12 |
| True Phishing | 14 | 313 |
| Class | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| 0 (Not phishing) | 0.9833 | 0.9856 | 0.9845 | 835 |
| 1 (Phishing) | 0.9631 | 0.9572 | 0.9601 | 327 |
Confusion Matrix
| Pred Not Phishing | Pred Phishing | |
|---|---|---|
| True Not Phishing | 969 | 5 |
| True Phishing | 6 | 215 |
| Class | Precision | Recall | F1-score | Support |
|---|---|---|---|---|
| 0 (Not phishing) | 0.9939 | 0.9949 | 0.9944 | 974 |
| 1 (Phishing) | 0.9773 | 0.9729 | 0.9751 | 221 |
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública, co-financed by the EU – NextGenerationEU, within the framework of the project Desarrollo de Modelos ALIA.
@misc{gplsi-mroberta-fraudephishing,
author = {Martínez-Murillo, Iván and Consuegra-Ayala, Juan Pablo and Bonora, Mar and Sepúlveda-Torres, Robiert},
title = {mRoBERTa_FT1_DFT1_fraude_phishing: Fine-tuned model for phishing detection},
year = {2025},
howpublished = {\url{https://huggingface.co/gplsi/mRoBERTa_FT1_DFT1_fraude_phishing}},
note = {Accessed: 2025-10-03}
}
Base model
BSC-LT/mRoBERTa