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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
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Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Generative AI and Large Language Models

In this chapter, we will explore recipes that use the generative aspect of the transformer models to generate text. As we touched upon the same in Chapter 8, Transformers and Their Applications, the generative aspect of the transformer models uses the decoder component of the transformer network. The decoder component is responsible for generating text based on the provided context.

With the advent of the General Purpose Transformers (GPT) family of Large Language Models (LLMs), these have only grown in size and capability with each new version. LLMs such as GPT-4 have been trained on large corpora of text and can match or beat their state-of-the-art counterparts in many NLP tasks. These LLMs have also built upon their generational capability and they can be instructed to generate text based on human prompting.

We will use generative models based on the transformer architecture for our recipes.

This chapter contains the following recipes...

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