Tucano: Advancing Neural Text Generation for Portuguese

TL;DR

This paper introduces Tucano, a series of Portuguese language models trained on a large, deduplicated corpus, demonstrating competitive performance and highlighting evaluation limitations in Portuguese NLP.

Contribution

The study presents GigaVerbo, a large Portuguese text corpus, and trains Tucano models that outperform similar-sized models, advancing neural text generation for Portuguese.

Findings

Tucano models perform equal or better than comparable models on Portuguese benchmarks.

Evaluation metrics show limited correlation with training data scale.

Open-source release of models and resources for community use.

Abstract

Significant advances have been made in natural language processing in recent years. However, our current deep learning approach to language modeling requires substantial resources in terms of data and computation. One of the side effects of this data-hungry paradigm is the current schism between languages, separating those considered high-resource, where most of the development happens and resources are available, and the low-resource ones, which struggle to attain the same level of performance and autonomy. This study aims to introduce a new set of resources to stimulate the future development of neural text generation in Portuguese. In this work, we document the development of GigaVerbo, a concatenation of deduplicated Portuguese text corpora amounting to 200 billion tokens. Via this corpus, we trained a series of decoder-transformers named Tucano. Our models perform equal or superior to other Portuguese and multilingual language models of similar size in several Portuguese benchmarks. The evaluation of our models also reveals that model performance on many currently available benchmarks used by the Portuguese NLP community has little to no correlation with the scaling of token ingestion during training, highlighting the limitations of such evaluations when it comes to the assessment of Portuguese generative language models. All derivatives of our study are openly released on GitHub and Hugging Face. See https://nkluge-correa.github.io/Tucano/