This repository contains the code and experiments for a paper studying how to fine-tune BERT for text classification tasks. The work explores different fine-tuning strategies and hyperparameters for BERT, a pre-trained language model, when adapting it for downstream classification tasks. It supports multiple datasets including Sogou News and other text classification benchmarks, and provides checkpoints compatible with the fastNLP library’s BertEmbedding module.