In this paper we present PeLLE, a family of large language models based on
the RoBERTa architecture, for Brazilian Portuguese, trained on curated, open
data from the Carolina corpus. Aiming at reproducible results, we describe
details of the pretraining of the models. We also evaluate PeLLE models against
a set of existing multilingual and PT-BR refined pretrained Transformer-based
LLM encoders, contrasting performance of large versus smaller-but-curated
pretrained models in several downstream tasks. We conclude that several tasks
perform better with larger models, but some tasks benefit from
smaller-but-curated data in its pretraining.

该论文介绍了基于 RoBERTa 架构的 PeLLE 模型系列，用于巴西葡萄牙语，使用来自 Carolina 语料库的筛选的开放数据进行训练。我们描述了模型的预训练细节，通过对比大型与精选预训练模型在多个下游任务中的性能，我们评估了 PeLLE 模型。我们得出结论，更大的模型在一些任务中表现更好，但一些任务从使用精选的数据进行预训练中受益。