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

Index

Symbols

345M-parameter GPT-2 model

downloading 509, 510

accuracy score 125

advanced prompt engineering 476, 477

ChatGPT prompts, building 479

keywords, adding 478

knowledge base for ChatGPT, building 477

knowledge base for GPT-4, building 477

moderation 479

quality control 479

user requests, parsing 478

AI-created content 458

model list 460, 461

opportunities 459

Allen Institute for AI

reference link 257

AllenNLP 343

URL 343

Amazon Web Services (AWS) 1, 12, 392

artificial intelligence, properties

computing power 6

data 5

model architecture 5

prompt engineering 6

attention heads 493

attention masks

creating 76

audio files

transcribing, with Whisper 483, 484

Automated Machine Learning (AutoML) 120

automatic question generation 303, 304

BERT-based transformer

architecture 258

basic samples 261-267

difficult samples 267-273

running...

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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

Index

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Authors (1)