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While large language models a la BERT are used ubiquitously in NLP,pretraining them is considered a luxury that only a few well-funded industrylabs can afford. How can one train such models with a more modest budget? Wepresent a recipe for pretraining a masked language model in 24 hours using asingle low-end deep learning server. We demonstrate that through a combinationof software optimizations, design choices, and hyperparameter tuning, it ispossible to produce models that are competitive with BERT-base on GLUE tasks ata fraction of the original pretraining cost.

How to Train BERT with an Academic Budget