Summary
BERT brings bidirectional attention to transformers. Predicting sequences from left to right and masking the future tokens to train a model has serious limitations. If the masked sequence contains the meaning we are looking for, the model will produce errors. BERT attends to all of the tokens of a sequence at the same time.
We explored the architecture of BERT, which only uses the encoder stack of transformers. 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 built a fine-tuning BERT model for an Acceptability Judgement downstream task. The fine-tuning process went through all phases of the process. First, we loaded the dataset and loaded the necessary pretrained modules of the model. Then the model was trained, and its performance measured.
Fine-tuning a pretrained model takes fewer machine resources than training downstream tasks from scratch. Fine-tuned models can perform a variety of tasks. BERT proves that we can pretrain a model on two tasks only, which is remarkable in itself. But producing a multitask fine-tuned model based on the trained parameters of the BERT pretrained model is extraordinary. OpenAI GPT had worked on this approach before, but BERT took it to another level!
In this chapter, we fine-tuned a BERT model. In the next chapter, Chapter 3, Pretraining a RoBERTa Model from Scratch, we will dig deeper into the BERT framework and build a pretraining BERT-like model from scratch.
