You're reading from Transformers for Natural Language Processing - Second Edition

Product type Book

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Pages 602 pages

Edition 2nd Edition

Languages

Concepts

Mobile Application Development

Author (1):

Denis Rothman

Table of Contents (25) Chapters

Preface

1. What are Transformers?

2. Getting Started with the Architecture of the Transformer Model

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

Computer vision

This book is about NLP, not computer vision. However, in the previous section, we implemented general purpose sequences that can be applied to many domains. Computer vision is one of them.

The title of the article by Dosovitskiy et al. (2021) says it all: An image is worth 16x16 words: Transformers for Image Recognition at Scale. The authors processed an image as sequences. The results proved their point.

Google has made vision transformers available in a Colaboratory notebook. Open Vision_Transformer_MLP_Mixer.ipynb in the Chapter16 directory of this book’s GitHub repository.

Open Vision_Transformer_MLP_Mixer.ipynb contains a transformer computer vision model in JAX(). JAX combines Autograd and XLA. JAX can differentiate Python and NumPy functions. JAX speeds up Python and NumPy by using compilation techniques and parallelization.

The notebook is self-explanatory. You can explore it to see how it works. However, bear in mind that when Industry...