Papers › How to Fine-Tune BERT for Text Classification?

How to Fine-Tune BERT for Text Classification?

14 May 2019arXiv:1905.05583archive 2025-07-28

Chi Sun, Xipeng Qiu, Yige Xu, Xuanjing Huang

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.

Code

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