{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/how-to-train-bert-with-an-academic-budget","title":"How to Train BERT with an Academic Budget","arxiv_id":"2104.07705","date":"2021-04-15","proceeding":"EMNLP 2021 11","authors":["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.","url_abs":"https://arxiv.org/abs/2104.07705v2","url_pdf":"https://arxiv.org/pdf/2104.07705v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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