Book Image

Mastering Transformers

By : Savas Yildirim, Meysam Asgari- Chenaghlu
Book Image

Mastering Transformers

By: Savas Yildirim, Meysam Asgari- Chenaghlu

Overview of this book

Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. 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. This book also 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. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.
Table of Contents (16 chapters)
1
Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications
4
Section 2: Transformer Models – From Autoencoding to Autoregressive Models
10
Section 3: Advanced Topics

Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications

In this section, you will learn about all aspects of Transformers at an introductory level. You will write your first hello-world program with Transformers by loading community-provided pre-trained language models and running the related code with or without a GPU. Installing and utilizing the tensorflow, pytorch, conda, transformers, and sentenceTransformers libraries will also be explained in detail in this section.

This section comprises the following chapters:

  • Chapter 1, From Bag-of-Words to the Transformers
  • Chapter 2, A Hands-On Introduction to the Subject