# [1907.11692] RoBERTa: A Robustly Optimized BERT Pretraining Approach
Abstract page for arXiv paper 1907.11692: RoBERTa: A Robustly Optimized BERT Pretraining Approach

# Computer Science > Computation and Language

[Submitted on 26 Jul 2019]

# Title:RoBERTa: A Robustly Optimized BERT Pretraining Approach

Authors: Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov
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> Abstract:Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.

Subjects: | Computation and Language (cs.CL)
Cite as: | arXiv:1907.11692 [cs.CL]
| (or arXiv:1907.11692v1 [cs.CL] for this version)
| https://doi.org/10.48550/arXiv.1907.11692 arXiv-issued DOI via DataCite

## Submission history

From: Myle Ott [view email]
[v1] Fri, 26 Jul 2019 17:48:29 UTC (45 KB)
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