PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data
This work addresses the problem of limited Portuguese language data for NLP researchers and practitioners, though it is incremental as it adapts an existing method to a new dataset.
The authors tackled the lack of Portuguese NLP resources by pretraining a T5 model on the BrWac corpus, showing significantly better performance than original T5 models and highlighting the benefits of a Portuguese vocabulary.
In natural language processing (NLP), there is a need for more resources in Portuguese, since much of the data used in the state-of-the-art research is in other languages. In this paper, we pretrain a T5 model on the BrWac corpus, an extensive collection of web pages in Portuguese, and evaluate its performance against other Portuguese pretrained models and multilingual models on three different tasks. We show that our Portuguese pretrained models have significantly better performance over the original T5 models. Moreover, we demonstrate the positive impact of using a Portuguese vocabulary. Our code and models are available at https://github.com/unicamp-dl/PTT5.