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

Transformer-based recommender systems

Transformer models learn sequences. Learning language sequences is a great place to start considering the billions of messages posted on social media and cloud platforms each day. Consumer behaviors, images, and sounds can also be represented in sequences.

In this section, we will first create a general-purpose sequence graph and then build a general-purpose transformer-based recommender in Google Colaboratory. We will then see how to deploy them in metahumans.

Let’s first define general-purpose sequences.

General-purpose sequences

Many activities can be represented by entities and links between them. They are thus organized in sequences. For example, a video on YouTube can be an entity A, and the link can be the behavior of a person going from video A to video E.

Another example is a bad fever being an entity F, and the link being the inference a doctor may make leading to a micro-decision B. The purchase of product...

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

Transformer-based recommender systems

General-purpose sequences

Unlock this book and the full library FREE for 7 days

Authors (1)