PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data

Abstract

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.

References in corpus (3)

• Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language
• BERTje: A Dutch BERT Model
• Multilingual is not enough: BERT for Finnish

Cited by in corpus (5)

• mT5: A massively multilingual pre-trained text-to-text transformer
• AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
• mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
• DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment
• A cost-benefit analysis of cross-lingual transfer methods