Having detailed the architecture of Transformer models, including attention mechanisms and the encoder-decoder structure in prior chapters, we now shift focus to putting these concepts into practice. This chapter addresses the essential steps involved in training and implementing Transformer models.

You will learn about preparing data for Transformers, covering common tokenization techniques like Byte Pair Encoding (BPE) and how to create properly formatted input batches, including padding and attention masks. We will then examine the training process itself, discussing appropriate loss functions (such as cross-entropy), optimization algorithms often used with Transformers (like Adam), learning rate scheduling techniques, and regularization methods like dropout. Finally, we will provide an overview of assembling the components discussed earlier into a basic working model and briefly introduce the use of libraries that offer pre-trained Transformer implementations.