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.
@article{arxiv.1905.05583,
title = {How to Fine-Tune BERT for Text Classification?},
author = {Chi Sun and Xipeng Qiu and Yige Xu and Xuanjing Huang},
journal= {arXiv preprint arXiv:1905.05583},
year = {2020}
}