This paper investigates different fine-tuning methods for BERT on text classification tasks and proposes a general solution for BERT fine-tuning. The authors conduct extensive experiments to analyze BERT's performance on eight widely-studied text classification datasets and achieve new state-of-the-art results. They explore three main approaches: fine-tuning strategies, further pre-training, and multi-task fine-tuning. Fine-tuning strategies involve selecting the most effective layers for text classification, handling long texts through truncation or hierarchical methods, and using layer-wise learning rates to prevent catastrophic forgetting. Further pre-training includes within-task, in-domain, and cross-domain pre-training, which significantly improves BERT's performance on text classification tasks. Multi-task fine-tuning is also explored, where BERT is fine-tuned on multiple related tasks simultaneously, which can enhance performance on single tasks. The authors also investigate the effectiveness of BERT on small datasets and find that pre-trained BERT performs well even with limited data. They compare BERT with other models such as CNN-based, RNN-based, and feature-based transfer learning methods, and find that BERT outperforms these models on most tasks. Additionally, they test BERT on the BERT-LARGE model and find that task-specific further pre-training leads to state-of-the-art results. The paper concludes that BERT is a powerful model for text classification, and the proposed fine-tuning methods significantly improve its performance. The authors achieve state-of-the-art results on eight widely-studied text classification datasets, demonstrating the effectiveness of their approach.This paper investigates different fine-tuning methods for BERT on text classification tasks and proposes a general solution for BERT fine-tuning. The authors conduct extensive experiments to analyze BERT's performance on eight widely-studied text classification datasets and achieve new state-of-the-art results. They explore three main approaches: fine-tuning strategies, further pre-training, and multi-task fine-tuning. Fine-tuning strategies involve selecting the most effective layers for text classification, handling long texts through truncation or hierarchical methods, and using layer-wise learning rates to prevent catastrophic forgetting. Further pre-training includes within-task, in-domain, and cross-domain pre-training, which significantly improves BERT's performance on text classification tasks. Multi-task fine-tuning is also explored, where BERT is fine-tuned on multiple related tasks simultaneously, which can enhance performance on single tasks. The authors also investigate the effectiveness of BERT on small datasets and find that pre-trained BERT performs well even with limited data. They compare BERT with other models such as CNN-based, RNN-based, and feature-based transfer learning methods, and find that BERT outperforms these models on most tasks. Additionally, they test BERT on the BERT-LARGE model and find that task-specific further pre-training leads to state-of-the-art results. The paper concludes that BERT is a powerful model for text classification, and the proposed fine-tuning methods significantly improve its performance. The authors achieve state-of-the-art results on eight widely-studied text classification datasets, demonstrating the effectiveness of their approach.