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

View More author details

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

Step 4: Checking the version of TensorFlow

The GPT-2 345M transformer model provided by OpenAI uses TensorFlow 1.x. This will lead to several warnings when running the program. However, we will ignore them and run at full speed on the thin ice of training GPT models ourselves with our modest machines.

In the 2020s, GPT models have reached 175 billion parameters, making it impossible for us to train them ourselves efficiently without having access to a supercomputer. The number of parameters will only continue to increase.

The corporate giants’ research labs, such as Facebook AI and OpenAI, and Google Research/Brain, are speeding toward super-transformers and are leaving what they can for us to learn and understand. But, unfortunately, they do not have time to go back and update all the models they share. However, we still have this notebook!

TensorFlow 2.x is the latest TensorFlow version. However, older programs can still be helpful. This is one reason why Google...

The rest of the chapter is locked

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

Step 4: Checking the version of TensorFlow

Unlock this book and the full library FREE for 7 days

Authors (1)