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Create 1910.01108.atom
Browse files- arxiv/1910.01108.atom +54 -0
arxiv/1910.01108.atom
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<?xml version="1.0" encoding="UTF-8"?>
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<feed xmlns="http://www.w3.org/2005/Atom">
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<link href="http://arxiv.org/api/query?search_query%3D%26id_list%3D1910.01108%26start%3D0%26max_results%3D1" rel="self" type="application/atom+xml"/>
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<title type="html">ArXiv Query: search_query=&id_list=1910.01108&start=0&max_results=1</title>
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<id>http://arxiv.org/api/GrfVMsjRJ5QfXT4borMr9RuzWhk</id>
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<updated>2023-06-09T00:00:00-04:00</updated>
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<entry>
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<id>http://arxiv.org/abs/1910.01108v4</id>
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<updated>2020-03-01T02:57:50Z</updated>
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<published>2019-10-02T17:56:28Z</published>
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<title>DistilBERT, a distilled version of BERT: smaller, faster, cheaper and
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lighter</title>
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<summary> As Transfer Learning from large-scale pre-trained models becomes more
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prevalent in Natural Language Processing (NLP), operating these large models in
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on-the-edge and/or under constrained computational training or inference
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budgets remains challenging. In this work, we propose a method to pre-train a
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smaller general-purpose language representation model, called DistilBERT, which
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can then be fine-tuned with good performances on a wide range of tasks like its
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larger counterparts. While most prior work investigated the use of distillation
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for building task-specific models, we leverage knowledge distillation during
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the pre-training phase and show that it is possible to reduce the size of a
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BERT model by 40%, while retaining 97% of its language understanding
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capabilities and being 60% faster. To leverage the inductive biases learned by
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larger models during pre-training, we introduce a triple loss combining
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language modeling, distillation and cosine-distance losses. Our smaller, faster
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and lighter model is cheaper to pre-train and we demonstrate its capabilities
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for on-device computations in a proof-of-concept experiment and a comparative
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on-device study.
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</summary>
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<author>
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<name>Victor Sanh</name>
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</author>
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<author>
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<name>Lysandre Debut</name>
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</author>
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<author>
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<name>Julien Chaumond</name>
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</author>
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<author>
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<name>Thomas Wolf</name>
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</author>
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<arxiv:comment xmlns:arxiv="http://arxiv.org/schemas/atom">February 2020 - Revision: fix bug in evaluation metrics, updated
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metrics, argumentation unchanged. 5 pages, 1 figure, 4 tables. Accepted at
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the 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing
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- NeurIPS 2019</arxiv:comment>
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<link href="http://arxiv.org/abs/1910.01108v4" rel="alternate" type="text/html"/>
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<link title="pdf" href="http://arxiv.org/pdf/1910.01108v4" rel="related" type="application/pdf"/>
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</entry>
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