Semantic Scholar Open Access 2019 1745 sitasi

How to Fine-Tune BERT for Text Classification?

Chi Sun Xipeng Qiu Yige Xu Xuanjing Huang

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Chi Sun

Xipeng Qiu

Yige Xu

Xuanjing Huang

Format Sitasi

APA MLA BibTeX

Sun, C., Qiu, X., Xu, Y., Huang, X. (2019). How to Fine-Tune BERT for Text Classification?. https://doi.org/10.1007/978-3-030-32381-3_16

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Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 1745×
Sumber Database: Semantic Scholar
DOI: 10.1007/978-3-030-32381-3_16
Akses: Open Access ✓