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 $6\times$ times less for BERT Base and $9\times$ 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.
翻译:在本文中,我们提出极端BERT,这是加速和定制BERT预培训的工具包,我们的目标是为研究界和工业界提供一个方便使用的BERT预培训工具包,因此,在资源有限的情况下,对通用语言模型进行定制数据集的预培训是负担得起的。 实验表明,为了达到相同或更好的GLUE分数,我们的工具包的时间成本比BERT基地低6倍以上,与原始的BERT文件相比,BERT大区则低9倍。 文件和代码在https://github.com/extreme-bert/extreme-bert上根据Apache-2.0许可证发布。