[1910.01108] DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Computer Science > Computation and Language

arXiv:1910.01108 (cs)

[Submitted on 2 Oct 2019 (v1), last revised 1 Mar 2020 (this version, v4)]

Title:DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Authors:Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf

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Abstract: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.

Comments: | 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

Subjects: | Computation and Language (cs.CL) Cite as: | arXiv:1910.01108 [cs.CL] (or arXiv:1910.01108v4 [cs.CL] for this version) https://doi.org/10.48550/arXiv.1910.01108 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Victor Sanh [view email] [v1] Wed, 2 Oct 2019 17:56:28 UTC (275 KB) [v2] Wed, 16 Oct 2019 14:52:02 UTC (275 KB) [v3] Fri, 24 Jan 2020 16:58:52 UTC (276 KB) [v4] Sun, 1 Mar 2020 02:57:50 UTC (276 KB)

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