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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

Chapter 3: Autoencoding Language Models

In the previous chapter, we looked at and studied how a typical Transformer model can be used by HuggingFace's Transformers. So far, all the topics have included how to use pre-defined or pre-built models and less information has been given about specific models and their training.

In this chapter, we will gain knowledge of how we can train autoencoding language models on any given language from scratch. This training will include pre-training and task-specific training of the models. First, we will start with basic knowledge about the BERT model and how it works. Then we will train the language model using a simple and small corpus. Afterward, we will look at how the model can be used inside any Keras model.

For an overview of what will be learned in this chapter, we will discuss the following topics:

  • BERT – one of the autoencoding language models
  • Autoencoding language model training for any language
  • Sharing...
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