Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, the pre-trained language models are usually computationally expensive and memory intensive, so it is difficult to effectively execute them on resource-restricted devices. To accelerate inference and reduce model size while maintaining accuracy, we firstly propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large "teacher" BERT can be well transferred to a small "student" TinyBERT. Moreover, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pre-training and task-specific learning stages. This framework ensures that TinyBERT can capture the general-domain as well as the task-specific knowledge in BERT. TinyBERT 1 is empirically effective and achieves comparable results with BERT on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference. TinyBERT is also significantly better than state-of-the-art baselines on BERT distillation, with only ∼28% parameters and ∼31% inference time of them. * Authors contribute equally. † This work is done when Xiaoqi Jiao is an intern at Huawei Noah's Ark Lab. 1 Our code and models will be made publicly available.
TinyBERT REF introduces a two-stage learning framework which performs transformer distillation at both pretraining and task-specific stages.
TinyBERT: Distilling BERT for Natural Language Understanding
ArXiv
ArXiv
Computer Science