Fine-Tuning BERT Models

In Chapter 1, Getting Started with the Model Architecture of the Transformer, we defined the building blocks of the architecture of the original Transformer. Think of the original Transformer as a model built with LEGO^® bricks. The construction set contains bricks such as encoders, decoders, embedding layers, positional encoding methods, multi-head attention layers, masked multi-head attention layers, post-layer normalization, feed-forward sub-layers, and linear output layers. The bricks come in various sizes and forms. You can spend hours building all sorts of models using the same building kit! Some constructions will only require some of the bricks. Other constructions will add a new piece, just like when we obtain additional bricks for a model built using LEGO^® components.

BERT added a new piece to the Transformer building kit: a bidirectional multi-head attention sub-layer. When we humans are having problems understanding a sentence, we do not just look at the past words. BERT, like us, looks at all the words in the same sentence at the same time.

In this chapter, we will first explore the architecture of Bidirectional Encoder Representations from Transformers (BERT). BERT only uses the blocks of the encoders of the Transformer in a novel way and does not use the decoder stack.

Then we will fine-tune a pretrained BERT model. The BERT model we will fine-tune was trained by a third party and uploaded to Hugging Face. Transformers can be pretrained. Then, a pretrained BERT, for example, can be fine-tuned on several NLP tasks. We will go through this fascinating experience of downstream Transformer usage using Hugging Face modules.

This chapter covers the following topics:

• Bidirectional Encoder Representations from Transformers (BERT)
• The architecture of BERT
• The two-step BERT framework
• Preparing the pretraining environment
• Defining pretraining encoder layers
• Defining fine-tuning
• Downstream multitasking
• Building a fine-tuning BERT model
• Loading an accessibility judgement dataset
• Creating attention masks
• BERT model configuration
• Measuring the performance of the fine-tuned model

Our first step will be to explore the background of the Transformer.