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How to Fine-Tune BERT for Text Classification?

Research paper by Chi Sun, Xipeng Qiu, Yige Xu, Xuanjing Huang

Indexed on: 15 May '19Published on: 14 May '19Published in: arXiv - Computer Science - Computation and Language



Abstract

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets.