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<title>[1907.11692] RoBERTa: A Robustly Optimized BERT Pretraining Approach</title>
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<meta property=og:description content="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.">
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<meta name=citation_title content="RoBERTa: A Robustly Optimized BERT Pretraining Approach"><meta name=citation_author content="Liu, Yinhan"><meta name=citation_author content="Ott, Myle"><meta name=citation_author content="Goyal, Naman"><meta name=citation_author content="Du, Jingfei"><meta name=citation_author content="Joshi, Mandar"><meta name=citation_author content="Chen, Danqi"><meta name=citation_author content="Levy, Omer"><meta name=citation_author content="Lewis, Mike"><meta name=citation_author content="Zettlemoyer, Luke"><meta name=citation_author content="Stoyanov, Veselin"><meta name=citation_date content=2019/07/26><meta name=citation_online_date content=2019/07/26><meta name=citation_pdf_url content=https://arxiv.org/pdf/1907.11692><meta name=citation_arxiv_id content=1907.11692><meta name=citation_abstract content="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.">
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<h1>Computer Science &gt; Computation and Language</h1>
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<strong>arXiv:1907.11692</strong> (cs)
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<style>#abs{font-family:"Lucida Grande",Helvetica,Arial,sans-serif!important}#abs h1.title{display:block;font-size:1.8em!important;font-weight:700;margin-block-end:12px;margin-block-start:12px;margin-bottom:12px;margin-inline-end:0px;margin-inline-start:20px;margin-left:20px;margin-right:0px;margin-top:12px}#abs div.authors{font-size:1.2em;line-height:24px;margin-bottom:8px;margin-left:20px;margin-right:0px;margin-top:8px}#abs div.dateline{font-size:0.9em;font-style:italic;margin-bottom:6.5px;margin-left:20px;margin-right:0px;margin-top:6.5px}#abs blockquote.abstract{font-size:1.05em;margin-block-end:21.6px;margin-block-start:14.4px;margin-bottom:21.6px;margin-inline-end:40px;margin-inline-start:40px;background-color:white;border-left:0px;padding:0px}#abs div.metatable{font-size:0.95em!important;margin-bottom:19px;margin-left:20px;margin-right:0px;margin-top:0px;border:0px;padding:0px}#abs div.metatable tbody{vertical-align:middle}#abs tr{margin-top:0px;margin-bottom:0px}#abs td.tablecell{padding-top:0px;padding-bottom:0px;padding-right:6.5px;padding-left:0px;vertical-align:top;font-size:0.95em!important;margin-top:0px;margin-bottom:0px;border:0px}#abs td.tablecell.label{font-weight:400!important}#abs span.primary-subject{font-weight:700}#abs span.arxivid{font-weight:700}</style>
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[Submitted on 26 Jul 2019]</div>
<h1 class="title mathjax"><span class="descriptor sf-hidden">Title:</span>RoBERTa: A Robustly Optimized BERT Pretraining Approach</h1>
<div class=authors><span class="descriptor sf-hidden">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Liu,+Y" rel=nofollow>Yinhan Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Ott,+M" rel=nofollow>Myle Ott</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Goyal,+N" rel=nofollow>Naman Goyal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Du,+J" rel=nofollow>Jingfei Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Joshi,+M" rel=nofollow>Mandar Joshi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Chen,+D" rel=nofollow>Danqi Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Levy,+O" rel=nofollow>Omer Levy</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Lewis,+M" rel=nofollow>Mike Lewis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Zettlemoyer,+L" rel=nofollow>Luke Zettlemoyer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Stoyanov,+V" rel=nofollow>Veselin Stoyanov</a></div> <div id=download-button-info class=sf-hidden hidden>View a PDF of the paper titled RoBERTa: A Robustly Optimized BERT Pretraining Approach, by Yinhan Liu and 9 other authors</div>
<a class=mobile-submission-download href=https://arxiv.org/pdf/1907.11692>View PDF</a>
<blockquote class="abstract mathjax">
<span class="descriptor sf-hidden">Abstract:</span>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.
</blockquote>
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<span class=primary-subject>Computation and Language (cs.CL)</span></td>
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<td class="tablecell label">Cite as:</td>
<td class="tablecell arxivid"><span class=arxivid><a href=https://arxiv.org/abs/1907.11692>arXiv:1907.11692</a> [cs.CL]</span></td>
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<h2>Submission history</h2> From: Myle Ott [<a href=https://arxiv.org/show-email/980ef4cc/1907.11692 rel=nofollow>view email</a>] <br> <strong>[v1]</strong>
Fri, 26 Jul 2019 17:48:29 UTC (45 KB)<br>
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