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Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

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arxiv 2305.06721 v2 pith:MENTTJRE submitted 2023-05-11 cs.CL

João Rodrigues , Luís Gomes , João Silva , António Branco , Rodrigo Santos , Henrique Lopes Cardoso , Tomás Osório This is my paper · ORCID
classification cs.CL
keywords portuguesealbertinapt-brlanguagept-ptsetsdataencoding
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To advance the neural encoding of Portuguese (PT), and a fortiori the technological preparation of this language for the digital age, we developed a Transformer-based foundation model that sets a new state of the art in this respect for two of its variants, namely European Portuguese from Portugal (PT-PT) and American Portuguese from Brazil (PT-BR). To develop this encoder, which we named Albertina PT-*, a strong model was used as a starting point, DeBERTa, and its pre-training was done over data sets of Portuguese, namely over data sets we gathered for PT-PT and PT-BR, and over the brWaC corpus for PT-BR. The performance of Albertina and competing models was assessed by evaluating them on prominent downstream language processing tasks adapted for Portuguese. Both Albertina PT-PT and PT-BR versions are distributed free of charge and under the most permissive license possible and can be run on consumer-grade hardware, thus seeking to contribute to the advancement of research and innovation in language technology for Portuguese.

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Cited by 2 Pith papers

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  1. NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus

    cs.CL 2026-04 unverdicted novelty 7.0 of 10

    NorBERTo, a ModernBERT-style Portuguese encoder trained from scratch on the 331B-token Aurora-PT corpus, posts top scores on PLUE and ASSIN 2 entailment, but lower scores on semantic similarity.

  2. Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil

    cs.HC 2025-07 conditional novelty 4.0 of 10

    The paper introduces two embedding pipelines and a visualization tool that reveal latent clusters and label errors in the Museu Nacional dos Povos Indígenas digital collection.

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