EMNLP 2021main117 citations
How to Train BERT with an Academic Budget
Peter Izsak, Moshe Berchansky, Omer Levy
Abstract
While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget? We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep learning server. We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT-base on GLUE tasks at a fraction of the original pretraining cost.
BibTeX
@inproceedings{izsak-etal-2021-train,
title = "How to Train {BERT} with an Academic Budget",
author = "Izsak, Peter and
Berchansky, Moshe and
Levy, Omer",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.831/",
doi = "10.18653/v1/2021.emnlp-main.831",
pages = "10644--10652"
}