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

Translations with Trax

Google Brain developed Tensor2Tensor (T2T) to make deep learning development easier. T2T is an extension of TensorFlow and contains a library of deep learning models that contains many transformer examploes.

Although T2T was a good start, Google Brain then produced Trax, an end-to-end deep learning library. Trax contains a transformer model that can be applied to translations. The Google Brain team presently maintains Trax.

This section will focus on the minimum functions to initialize the English-German problem described by Vaswani et al. (2017) to illustrate the Transformer’s performance.

We will be using preprocessed English and German datasets to show that the Transformer architecture is language-agnostic.

Open Trax_Translation.ipynb.

We will begin by installing the modules we need.