You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Length 602 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Denis Rothman

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Table of Contents (25) Chapters

Preface

1. What are Transformers?

2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER

3. Fine-Tuning BERT Models

4. Pretraining a RoBERTa Model from Scratch

5. Downstream NLP Tasks with Transformers

6. Machine Translation with the Transformer

7. The Rise of Suprahuman Transformers with GPT-3 Engines

8. Applying Transformers to Legal and Financial Documents for AI Text Summarization

9. Matching Tokenizers and Datasets

10. Semantic Role Labeling with BERT-Based Transformers

11. Let Your Data Do the Talking: Story, Questions, and Answers

12. Detecting Customer Emotions to Make Predictions

13. Analyzing Fake News with Transformers

14. Interpreting Black Box Transformer Models

15. From NLP to Task-Agnostic Transformer Models

16. The Emergence of Transformer-Driven Copilots

17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4

18. Other Books You May Enjoy

19. Index

Appendix I — Terminology of Transformer Models

1. Appendix II — Hardware Constraints for Transformer Models

2. Appendix III — Generic Text Completion with GPT-2

3. Appendix IV — Custom Text Completion with GPT-2

4. Appendix V — Answers to the Questions

Transformer visualization with BertViz

Jesse Vig’s article, A Multiscale Visualization of Attention in the Transformer Model, 2019, recognizes the effectiveness of transformer models. However, Jesse Vig explains that deciphering the attention mechanism is challenging. The paper describes the process of BertViz, a visualization tool.

BertViz can visualize attention head activity and interpret a transformer model’s behavior.

BertViz was first designed to visualize BERT and GPT-3 models. In this section, we will visualize the activity of a BERT model.

Let’s now install and run BertViz.

Running BertViz

It only takes five steps to visualize transformer attention heads and interact with them.

Open the BertViz.ipynb notebook in the Chapter14 directory in the GitHub repository of this book.

The first step is to install BertViz and the requirements.

Step 1: Installing BertViz and importing the modules

The notebook installs BertViz, Hugging...

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You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Table of Contents (25) Chapters

Transformer visualization with BertViz

Running BertViz

Step 1: Installing BertViz and importing the modules

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Authors (1)