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Mastering Transformers

You're reading from   Mastering Transformers Build state-of-the-art models from scratch with advanced natural language processing techniques

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801077651
Length 374 pages
Edition 1st Edition
Languages
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Authors (2):
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Savaş Yıldırım Savaş Yıldırım
Author Profile Icon Savaş Yıldırım
Savaş Yıldırım
Meysam Asgari- Chenaghlu Meysam Asgari- Chenaghlu
Author Profile Icon Meysam Asgari- Chenaghlu
Meysam Asgari- Chenaghlu
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Toc

Table of Contents (16) Chapters Close

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 FREE CHAPTER 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

Technical requirements

We will be using Jupyter Notebook, Python, and Dockerfile to run our coding exercises, which will require Python 3.6.0. The following packages need to be installed:

  • TensorFlow
  • PyTorch
  • Transformer >=4.00
  • fastAPI
  • Docker
  • Locust

Now, let's get started!

All the notebooks for the coding exercises in this chapter will be available at the following GitHub link: https://github.com/PacktPublishing/Mastering-Transformers/tree/main/CH10.

Check out the following link to see the Code in Action video: https://bit.ly/375TOPO

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