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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

Text summarization with T5

NLP summarizing tasks extract succinct parts of a text. This section will start by presenting the Hugging Face resources we will use in this chapter. Then we will initialize a T5-large transformer model. Finally, we will see how to use T5 to summarize any document, including legal and corporate documents.

Let’s begin by introducing Hugging Face’s framework.

Hugging Face

Hugging Face designed a framework to implement Transformers at a higher level. We used Hugging Face to fine-tune a BERT model in Chapter 3, Fine-Tuning BERT Models, and train a RoBERTa model in Chapter 4, Pretraining a RoBERTa Model from Scratch.

To expand our knowledge, we needed to explore other approaches, such as Trax, in Chapter 6, Machine Translation with the Transformer, and OpenAI’s models, in Chapter 7, The Rise of Suprahuman Transformers with GPT-3 Engines. This chapter will use Hugging Face’s framework again and explain more about the...

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