Book Image

Mastering Transformers

By : Savaş Yıldırım, Meysam Asgari- Chenaghlu
Book Image

Mastering Transformers

By: Savaş Yıldırım, 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

Chapter 4:Autoregressive and Other Language Models

We looked at details of Autoencoder (AE) language models in Chapter 3, Autoencoding Language Models, and studied how an AE language model can be trained from scratch. In the current chapter, you will see theoretical details of Autoregressive (AR) language models and learn how to pre-train them on your own corpus. You will learn how to pre-train any language model such as Generated Pre-trained Transformer 2 (GPT-2) on your own text and use it in various tasks such as Natural Language Generation (NLG). You will understand the basics of a Text-to-Text Transfer Transformer (T5) model and train a Multilingual T5 (mT5) model on your own Machine Translation (MT) data. After finishing this chapter, you will have an overview of AR language models and their various use cases in text2text applications, such as summarization, paraphrasing, and MT.

The following topics will be covered in this chapter:

  • Working with AR language models...