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.

Keywords

Leverage (statistics)Computer scienceInferenceLanguage understandingDistillationLanguage modelComputationTask (project management)Representation (politics)Artificial intelligenceEdge deviceMachine learningEnhanced Data Rates for GSM EvolutionNatural language processingAlgorithmEngineering

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Year
2019
Type
preprint
Citations
4539
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Closed

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Victor Sanh, Lysandre Debut, Julien Chaumond et al. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv (Cornell University) . https://doi.org/10.48550/arxiv.1910.01108

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DOI
10.48550/arxiv.1910.01108