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

ChatGPT Plus writes and comments on a program

In this section, ChatGPT Plus will do all the work: writing the code, commenting on the code, and providing an explanation.

IBM SPSS Decision Trees is a classification and decision tree tool designed for a decision-making system: https://www.ibm.com/products/spss-decision-trees.

However, for some projects, we do not need a complex program but a compact function to get the job done.

Open the following notebook, Chapter17, which is in the GitHub repository:

ChatGPT_Plus_writes_and_explains_classification.ipynb

Install and import OpenAI and enter the API key before running the notebook.

Designing the prompt

After installing scikit-learn as suggested by ChatGPT Plus, we submit two requests in sequence to ChatGPT Plus:

Provide a scikit-learn classification of the Iris dataset with some kind of matplotlib graph to describe the result. Don’t use OpenAI APIs.
Now write a detailed explanation...

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

ChatGPT Plus writes and comments on a program

Designing the prompt

Unlock this book and the full library FREE for 7 days

Authors (1)