TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing

By Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting-Wei Liu, Shijin Wang, Guoping Hu. 2020

51 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

Knowledge Distillation: A Survey (2020, methodology): “…of them belong to natural language understanding (NLU), and many of these KD methods for NLU are designed as the task-speciﬁc distillation (Tang et al. 2019; Turc et al. 2019; Mou et al. 2016) and multi-task distillation (Liu et al. 2019f; Yang et al. 2020c; Sanh et al. 2019; Clark et al. 2019).”
AmbiPun: Generating Humorous Puns with Ambiguous Context (2022, background): “Garimella et al. (2020) develops a model to ﬁll blanks in madlibs format and Yang et al. (2020) edit headlines to make them funny.”
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing (2021, methodology): “TextBrewer [286] Model Compression Supports Knowledge distillation methods.”