| Título: | RigoBERTa: A State of the Art Language Model For Spanish |
|---|---|
| Autores: | Alejandro Vaca Serrano, Guillem Garcia Subies, Helena Montoro Zamorano, Nuria Aldama Garcia, Doaa Samy, David Betancur Sanchez, Antonio Moreno Sandoval, Marta Guerrero Nieto, Alvaro Barbero Jimenez |
| Año: | 2022 |
Abstract
Abstract: This paper presents RigoBERTa, a State-of-the-Art Language Model for Spanish. RigoBERTa
is trained over a well-curated corpus formed up from different subcorpora with key features. It
follows the DeBERTa architecture, which has several advantages over other architectures of similar
size as BERT or RoBERTa. RigoBERTa performance is assessed over 13 NLU tasks in comparison with other available Spanish
language models, namely, MarIA, BERTIN and BETO. RigoBERTa outperformed the three models
in 10 out of the 13 tasks, achieving new ”State-of-the-Art” results..
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