Summary of Ptt5-v2: a Closer Look at Continued Pretraining Of T5 Models For the Portuguese Language, by Marcos Piau et al.
ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language
by Marcos Piau, Roberto Lotufo, Rodrigo Nogueira
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to adapting pre-trained language models for Portuguese, leveraging the T5 model and its continued pretraining on large-scale corpora. The authors develop a baseline set of settings and pretrain models up to 3B parameters, achieving state-of-the-art (SOTA) results on two downstream tasks: assin2 RTE and TweetSentBR. They then investigate the effects of different pretraining configurations, including data quality, optimization strategies, and multi-epoch pretraining, finding that these variations have a subtle impact compared to the baseline. The authors release their pretrained checkpoints and finetuned rerankers on HuggingFace, contributing to the development of language models for underrepresented languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving language models for languages other than English. Right now, most language models are trained on English texts and aren’t very good at understanding other languages. The authors developed a new way to train these models using Portuguese texts and achieved state-of-the-art results in two tasks. They also explored different ways of training the model and found that some methods work slightly better than others. The authors shared their trained models and code with the research community, which can help others develop language models for other languages. |
Keywords
» Artificial intelligence » Optimization » Pretraining » T5
