How to Fine-Tune BERT for Text Classification?

TL;DR

This paper investigates various fine-tuning methods for BERT in text classification, providing a comprehensive solution that achieves state-of-the-art results across multiple datasets.

Contribution

It offers a systematic analysis of BERT fine-tuning techniques and proposes a general approach that improves performance on text classification tasks.

Findings

Achieved new state-of-the-art results on eight datasets.

Identified effective fine-tuning strategies for BERT.

Provided practical guidelines for BERT adaptation.

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.