Abstract

Large pretrained models have achieved great success in many natural language processing tasks. However, when they are applied in specific domains, these models suffer from domain shift and bring challenges in fine-tuning and online serving for latency and capacity constraints. In this paper, we present a general approach to developing small, fast and effective pretrained models for specific domains. This is achieved by adapting the off-the-shelf general pretrained models and performing task-agnostic knowledge distillation in target domains. Specifically, we propose domain-specific vocabulary expansion in the adaptation stage and employ corpus level occurrence probability to choose the size of incremental vocabulary automatically. Then we systematically explore different strategies to compress the large pretrained models for specific domains. We conduct our experiments in the biomedical and computer science domain. The experimental results demonstrate that our approach achieves better performance over the $\text{BERT}_{\text{BASE}}$ model in domain-specific tasks while 3.3× smaller and 5.1× faster than $\text{BERT}_{\text{BASE}}$ . The code and pretrained models are available at htt