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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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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 11: Attention Visualization and Experiment Tracking

In this chapter, we will cover two different technical concepts, attention visualization and experiment tracking, and we will practice them through sophisticated tools such as exBERT and BertViz. These tools provide important functions for interpretability and explainability. First, we will discuss how to visualize the inner parts of attention by utilizing the tools. It is important to interpret the learned representations and to understand the information encoded by self-attention heads in the Transformer. We will see that certain heads correspond to a certain aspect of syntax or semantics. Secondly, we will learn how to track experiments by logging and then monitoring by using TensorBoard and Weights & Biases (W&B). These tools enable us to efficiently host and track experimental results such as loss or other metrics, which helps us to optimize model training. You will learn how to use exBERT and BertViz to see the...

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