  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.
