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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

Summary

In this chapter, you learned the basics of serving Transformer models using fastAPI. You also learned how to serve models in a more advanced and efficient way, such as by using TFX. You then studied the basics of load testing and creating users. Making these users spawn in groups or one by one, and then reporting the results of stress testing, was another major topic of this chapter. After that, you studied the basics of Docker and how to package your application in the form of a Docker container. Finally, you learned how to serve Transformer-based models.

In the next chapter, you will learn about Transformer deconstruction, the model view, and monitoring training using various tools and techniques.

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