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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

Generating code using an LLM

In this recipe, we will explore how an LLM can be used to generate code. We will use two separate examples to check the breadth of coverage for the generation. We will also compare the output from two LLMs to observe how the generation varies across two different models. Applications of such methods are already incorporated in popular Integrated Development Environments (IDEs). Our goal here is to demonstrate a basic framework for how to use a pre-trained LLM to generate code snipped based on simple human-defined requirements.

Getting ready

We will use a model from Hugging Face as well as OpenAI in this recipe. Please refer to Model access under the Technical requirements section to complete the step to access the Llama and OpenAI models. You can use the 10.6_code_generation_with_llm.ipynb notebook from the code site if you want to work from an existing notebook. Please note that due to the compute requirements for this recipe, it might take a few...

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