Fine-tuning further trains a pretrained model on task-specific labeled data. It adapts an existing language model to a focused NLP task, often requiring less data, time, and expertise than training a model from scratch. section 6.2.4
Choose fine-tuning when the task needs more specialization than a general model provides:
- Use a relevant pretrained model when collecting large amounts of labeled data is difficult. section 6.2.4
- Expect stronger performance than zero-shot learning on nuanced or domain-specific tasks, provided the model and labels fit the problem. section 6.2.4
- Prefer training from scratch only when extensive labeled data, time, effort, and expertise are available for deeper customization. section 6.2.4
Adapting BERT for an NLP task
A pretrained BERT model available through Hugging Face can be further trained with a comparatively small, task-specific dataset. The result may better reflect the language and requirements of a particular sentiment-analysis problem than a general zero-shot model. section 6.2.4
Fine-tuning is not automatically reliable. Results depend on the pretrained model’s relevance and the quality of labeled data, while incorrect adjustments can cause overfitting. Specialized libraries may still offer better accuracy, consistency, speed, or scalability for focused text-analysis tasks. section 6.2.4 section 5.1.3
