The essence of BERT fine-tuning is that it does not make the model learn from scratch; rather, it guides a "generalist" model that already possesses general linguistic knowledge to rapidly adapt to a specific "specialist" task. Among the key factors for success are targeted fine-tuning strategies and appropriate loss functions.