  In this paper, we introduce TextBrewer, an open-source knowledge distillation
toolkit designed for natural language processing. It works with different
neural network models and supports various kinds of supervised learning tasks,
such as text classification, reading comprehension, sequence labeling.
TextBrewer provides a simple and uniform workflow that enables quick setting up
of distillation experiments with highly flexible configurations. It offers a
set of predefined distillation methods and can be extended with custom code. As
a case study, we use TextBrewer to distill BERT on several typical NLP tasks.
With simple configurations, we achieve results that are comparable with or even
higher than the public distilled BERT models with similar numbers of
parameters. Our toolkit is available through: http://textbrewer.hfl-rc.com
