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Mastering Transformers

You're reading from  Mastering Transformers

Product type Book
Published in Sep 2021
Publisher Packt
ISBN-13 9781801077651
Pages 374 pages
Edition 1st Edition
Languages
Authors (2):
Savaş Yıldırım Savaş Yıldırım
Profile icon Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Profile icon Meysam Asgari- Chenaghlu
View More author details

Table of Contents (16) Chapters

Preface 1. Section 1: Introduction – Recent Developments in the Field, Installations, and Hello World Applications
2. Chapter 1: From Bag-of-Words to the Transformer 3. Chapter 2: A Hands-On Introduction to the Subject 4. Section 2: Transformer Models – From Autoencoding to Autoregressive Models
5. Chapter 3: Autoencoding Language Models 6. Chapter 4:Autoregressive and Other Language Models 7. Chapter 5: Fine-Tuning Language Models for Text Classification 8. Chapter 6: Fine-Tuning Language Models for Token Classification 9. Chapter 7: Text Representation 10. Section 3: Advanced Topics
11. Chapter 8: Working with Efficient Transformers 12. Chapter 9:Cross-Lingual and Multilingual Language Modeling 13. Chapter 10: Serving Transformer Models 14. Chapter 11: Attention Visualization and Experiment Tracking 15. Other Books You May Enjoy

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...
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