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Transformers for Natural Language Processing

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

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781803247335
Length 602 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (25) Chapters Close

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

Appendix III — Generic Text Completion with GPT-2

This appendix is the detailed explanation of the Generic text completion with GPT-2 section in Chapter 7, The Rise of Suprahuman Transformers with GPT-3 Engines. This section describes how to implement a GPT-2 transformer model for generic text complexion.

You can read the usage of this notebook directly in Chapter 7 or build the program and run it in this appendix to get more profound knowledge of how a GPT model works.

We will clone the OpenAI_GPT_2 repository, download the 345M-parameter GPT-2 transformer model, and interact with it. We will enter context sentences and analyze the text generated by the transformer. The goal is to see how it creates new content.

This section is divided into nine steps. Open OpenAI_GPT_2.ipynb in Google Colaboratory. The notebook is in the AppendixIII directory of the GitHub repository of this book. You will notice that the notebook is also divided into the same nine steps and cells...

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