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

Basic samples

Basic samples seem intuitively simple but can be tricky to analyze. Compound sentences, adjectives, adverbs, and modals are difficult to identify, even for non-expert humans.

Let’s begin with an easy sample for the transformer.

Sample 1

The first sample is long but relatively easy for the transformer:

Did Bob really think he could prepare a meal for 50 people in only a few hours?

Run the Sample 1 cell in SRL.ipynb:

prediction=predictor.predict(
    sentence="Did Bob really think he could prepare a meal for 50 people in only a few hours?"
)
head(prediction)

BERT SRL identified the four predicates; the verb for each one labeled the result as shown in this excerpt using the head(prediction) function:

Verb: Did [V: Did] Bob really think he could prepare a meal for 50 people in only a few hours ?
Verb: think Did [ARG0: Bob] [ARGM-ADV: really] [V: think] [ARG1: he could prepare a meal for 50 people in only a few hours...

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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

Basic samples

Sample 1

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Authors (1)