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

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. In 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing @ NeurIPS 2019, 2019.

Paper abstract bibtex

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.

@inproceedings{Sanh2019,
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.},
archivePrefix = {arXiv},
arxivId = {1910.01108},
author = {Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
booktitle = {5th Workshop on Energy Efficient Machine Learning and Cognitive Computing @ NeurIPS 2019},
eprint = {1910.01108},
file = {:Users/shanest/Documents/Library/Sanh et al/5th Workshop on Energy Efficient Machine Learning and Cognitive Computing @ NeurIPS 2019/Sanh et al. - 2019 - DistilBERT, a distilled version of BERT smaller, faster, cheaper and lighter.pdf:pdf},
keywords = {model},
title = {{DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}},
url = {http://arxiv.org/abs/1910.01108},
year = {2019}
}

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