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

Fine-tuning BERT

This section will fine-tune a BERT model to predict the downstream task of Acceptability Judgments and measure the predictions with the Matthews Correlation Coefficient (MCC), which will be explained in the Evaluating using Matthews Correlation Coefficient section of this chapter.

Open BERT_Fine_Tuning_Sentence_Classification_GPU.ipynb in Google Colab (make sure you have an email account). The notebook is in Chapter03 in the GitHub repository of this book.

The title of each cell in the notebook is also the same as or very close to the title of each subsection of this chapter.

We will first examine why transformer models must take hardware constraints into account.

Hardware constraints

Transformer models require multiprocessing hardware. Go to the Runtime menu in Google Colab, select Change runtime type, and select GPU in the Hardware Accelerator drop-down list.

Transformer models are hardware-driven. I recommend reading Appendix II, Hardware...