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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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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

Creating a confusion matrix plot

When working with machine learning models, for example, NLP classification models, creating a confusion matrix plot can be a very good tool to see the mistakes that the model makes to then further refine it. The model “confuses” one class for another, hence the name confusion matrix.

After working through this recipe, you will be able to create an SVM model, evaluate it, and then create a confusion matrix visualization that will tell you in detail which mistakes the model makes.

Getting ready

We will create an SVM classifier for the BBC news dataset using the sentence transformer model as the vectorizer. We will then use the ConfusionMatrixDisplay object to create a more informative confusion matrix. The classifier is the same as in the Chapter 4 recipe Using SVMs for supervised text classification.

The dataset is located at https://github.com/PacktPublishing/Python-Natural-Language-Processing-Cookbook-Second-Edition/tree/main...

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