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? FREE CHAPTER

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

An expanding universe of models

New transformer models, like new smartphones, emerge nearly every week. Some of these models are both mind-blowing and challenging for a project manager:

ERNIE is a continual pretraining framework that produces impressive results for language understanding.
Paper: https://arxiv.org/abs/1907.12412

Challenges: Hugging Face provides a model. Is it a full-blown model? Is it the one Baidu trained to exceed human baselines on the SuperGLUE Leaderboard (December 2021): https://super.gluebenchmark.com/leaderboard? Do we have access to the best one or just a toy model? What is the purpose of running AutoML on such small versions of models? Will we gain access to it on the Baidu platform or a similar one? How much will it cost?

SWITCH: A trillion-parameter model optimized with sparse modeling.
Paper: https://arxiv.org/abs/2101.03961

Challenges: The paper is fantastic. Where is the model? Will we ever have access...

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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

An expanding universe of models

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Authors (1)