REVIEW 3 cited by
Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*
read the original abstract
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.
Forward citations
Cited by 3 Pith papers
-
NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus
NorBERTo, a ModernBERT encoder trained on the largest open Portuguese corpus of 331B tokens, reports top encoder results on several PLUE and ASSIN 2 tasks.
-
LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification
Fine-tuned BERTimbau-LoRA achieves 87.6% accuracy and 0.87 macro-F1 on LegalBench-BR, outperforming commercial LLMs by 22-28 points and eliminating their systematic bias toward civil law on Brazilian legal classification.
-
NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.