ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT

arXiv2211.17201MaRDI QIDQ6419099

Jianlin Chen, Shizhe Diao, Tong Zhang, Rui Pan

Publication date: 30 November 2022

Abstract: In this paper, we present ExtremeBERT, a toolkit for accelerating and customizing BERT pretraining. Our goal is to provide an easy-to-use BERT pretraining toolkit for the research community and industry. Thus, the pretraining of popular language models on customized datasets is affordable with limited resources. Experiments show that, to achieve the same or better GLUE scores, the time cost of our toolkit is over $6imes$ times less for BERT Base and $9imes$ times less for BERT Large when compared with the original BERT paper. The documentation and code are released at https://github.com/extreme-bert/extreme-bert under the Apache-2.0 license.

Has companion code repository: https://github.com/extreme-bert/extreme-bert

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