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

Method 0: Trial and error

Question-answering seems very easy. Is that true? Let’s find out.

Open QA.ipynb, the Google Colab notebook we will be using in this chapter. We will run the notebook cell by cell.

Run the first cell to install Hugging Face’s transformers, the framework we will be implementing in this chapter:

!pip install -q transformers

Note: Hugging Face transformers continually evolve, updating libraries and modules to adapt to the market. If the default version doesn’t work, you might have to pin one with !pip install transformers==[version that runs with the other functions in the notebook].

We will now import Hugging Face’s pipeline, which contains many ready-to-use transformer resources. They provide high-level abstraction functions for the Hugging Face library resources to perform a wide range of tasks. We can access those NLP tasks through a simple API. The program was created on Google Colab. It recommended...