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

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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

Method 2: SRL first

The transformer could not find who was driving to go to Las Vegas and thought it was from Nat King Cole instead of Jo and Maria.

What went wrong? Can we see what the transformers think and obtain an explanation? To find out, let’s go back to semantic role modeling. If necessary, take a few minutes to review Chapter 10, Semantic Role Labeling with BERT-Based Transformers.

Let’s run the same sequence on AllenNLP in the Semantic Role Labeling section, https://demo.allennlp.org/semantic-role-labeling, to obtain a visual representation of the verb drove in our sequence by running the SRL BERT model we used in the previous chapter:

Graphical user interface, text, application Description automatically generated

Figure 11.2: SRL run on the text

SRL BERT found 19 frames. In this section, we focus on drove.

Note: The results may vary from one run to another or when AllenNLP updates the model versions.

We can see the problem. The argument of the verb drove is Jo and Maria. It seems that the inference...

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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

Method 2: SRL first

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Authors (1)