Summary
In this chapter, we explored the process of fine-tuning a transformer model. We achieved this by implementing the fine-tuning process of a pretrained Hugging Face BERT model.
We began by analyzing the architecture of BERT, which only uses the encoder stack of transformers and uses bidirectional attention. BERT was designed as a two-step framework. The first step of the framework is to pretrain a model. The second step is to fine-tune the model.
We then configured a fine-tuning BERT model for an acceptability judgment downstream task. The fine-tuning process went through all phases of the process.
We installed the Hugging Face transformers and considered the hardware constraints, including selecting CUDA as the device for torch. We retrieved the CoLA dataset from GitHub. We loaded and created in-domain (training data) sentences, label lists, and BERT tokens.
The training data was processed with the BERT tokenizer and other data preparation functions, including...
