Fine-Tuning Representation Models for Classification

This chapter covered several methods for fine-tuning pretrained models for specific classification tasks. Key techniques included:

Standard Fine-Tuning: Fine-tuning a BERT model, with the option to freeze certain layers to preserve learned representations.

Few-Shot Classification: Using a technique called SetFit to achieve high performance on tasks with very limited labeled data.

Continued Pretraining: Adapting a pretrained model to new data using its original training objective, masked language modeling.

Token-Level Classification: Performing named-entity recognition (NER), which classifies individual words rather than entire documents.