The book gives you an introduction to Transformers by showing you how to write your first hello-world program.
You’ll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you’ll explore
the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You’ll see
how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language
generation (NLG) problems, including text classification, token classification, and text representation.
Furthermore, this book helps you to learn efficient models for challenging problems, such as long-context
NLP tasks with limited computational capacity. You’ll also work with multilingual and cross-lingual problems,
optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability
and explainability. Finally, you’ll be able to deploy your transformer models in a production environment.