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arxiv/1910.01108.atom

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