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<title>[2412.13663] Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference</title>
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<meta name=citation_title content="Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference"><meta name=citation_author content="Warner, Benjamin"><meta name=citation_author content="Chaffin, Antoine"><meta name=citation_author content="Clavié, Benjamin"><meta name=citation_author content="Weller, Orion"><meta name=citation_author content="Hallström, Oskar"><meta name=citation_author content="Taghadouini, Said"><meta name=citation_author content="Gallagher, Alexis"><meta name=citation_author content="Biswas, Raja"><meta name=citation_author content="Ladhak, Faisal"><meta name=citation_author content="Aarsen, Tom"><meta name=citation_author content="Cooper, Nathan"><meta name=citation_author content="Adams, Griffin"><meta name=citation_author content="Howard, Jeremy"><meta name=citation_author content="Poli, Iacopo"><meta name=citation_date content=2024/12/18><meta name=citation_online_date content=2024/12/19><meta name=citation_pdf_url content=https://arxiv.org/pdf/2412.13663><meta name=citation_arxiv_id content=2412.13663><meta name=citation_abstract content="Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.">
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[Submitted on 18 Dec 2024 (<a href=https://arxiv.org/abs/2412.13663v1>v1</a>), last revised 19 Dec 2024 (this version, v2)]</div>
<h1 class="title mathjax"><span class="descriptor sf-hidden">Title:</span>Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference</h1>
<div class=authors><span class="descriptor sf-hidden">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Warner,+B" rel=nofollow>Benjamin Warner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Chaffin,+A" rel=nofollow>Antoine Chaffin</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Clavi%C3%A9,+B" rel=nofollow>Benjamin Clavié</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Weller,+O" rel=nofollow>Orion Weller</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Hallstr%C3%B6m,+O" rel=nofollow>Oskar Hallström</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Taghadouini,+S" rel=nofollow>Said Taghadouini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Gallagher,+A" rel=nofollow>Alexis Gallagher</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Biswas,+R" rel=nofollow>Raja Biswas</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Ladhak,+F" rel=nofollow>Faisal Ladhak</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Aarsen,+T" rel=nofollow>Tom Aarsen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Cooper,+N" rel=nofollow>Nathan Cooper</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Adams,+G" rel=nofollow>Griffin Adams</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Howard,+J" rel=nofollow>Jeremy Howard</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Poli,+I" rel=nofollow>Iacopo Poli</a></div> <div id=download-button-info hidden class=sf-hidden>View a PDF of the paper titled Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference, by Benjamin Warner and 13 other authors</div>
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<blockquote class="abstract mathjax">
<span class="descriptor sf-hidden">Abstract:</span>Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.
</blockquote>
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<span class=primary-subject>Computation and Language (cs.CL)</span>; Artificial Intelligence (cs.AI)</td>
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<td class="tablecell arxivid"><span class=arxivid><a href=https://arxiv.org/abs/2412.13663>arXiv:2412.13663</a> [cs.CL]</span></td>
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<td class="tablecell arxivdoi"> <a href=https://doi.org/10.48550/arXiv.2412.13663 id=arxiv-doi-link>https://doi.org/10.48550/arXiv.2412.13663</a><div class=button-and-tooltip>
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<h2>Submission history</h2> From: Benjamin Clavié [<a href=https://arxiv.org/show-email/28b3f0de/2412.13663 rel=nofollow>view email</a>] <br> <strong><a href=https://arxiv.org/abs/2412.13663v1 rel=nofollow>[v1]</a></strong>
Wed, 18 Dec 2024 09:39:44 UTC (81 KB)<br>
<strong>[v2]</strong>
Thu, 19 Dec 2024 06:32:26 UTC (81 KB)<br>
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