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Mastering spaCy

You're reading from   Mastering spaCy An end-to-end practical guide to implementing NLP applications using the Python ecosystem

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800563353
Length 356 pages
Edition 1st Edition
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Author (1):
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Duygu Altınok Duygu Altınok
Author Profile Icon Duygu Altınok
Duygu Altınok
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Getting Started with spaCy
2. Chapter 1: Getting Started with spaCy FREE CHAPTER 3. Chapter 2: Core Operations with spaCy 4. Section 2: spaCy Features
5. Chapter 3: Linguistic Features 6. Chapter 4: Rule-Based Matching 7. Chapter 5: Working with Word Vectors and Semantic Similarity 8. Chapter 6: Putting Everything Together: Semantic Parsing with spaCy 9. Section 3: Machine Learning with spaCy
10. Chapter 7: Customizing spaCy Models 11. Chapter 8: Text Classification with spaCy 12. Chapter 9: spaCy and Transformers 13. Chapter 10: Putting Everything Together: Designing Your Chatbot with spaCy 14. Other Books You May Enjoy

Chapter 7: Customizing spaCy Models

In this chapter, you will learn how to train, store, and use custom statistical pipeline components. First, we will discuss when exactly we should perform custom model training. Then, you will learn a fundamental step of model training – how to collect and label your own data.

In this chapter, you will also learn how to make the best use of Prodigy, the annotation tool. Next, you will learn how to update an existing statistical pipeline component with your own data. We will update the spaCy pipeline's named entity recognizer (NER) component with our own labeled data.

Finally, you will learn how to create a statistical pipeline component from scratch with your own data and labels. For this purpose, we will again train an NER model. This chapter takes you through a complete machine learning practice, including collecting data, annotating data, and training a model for information extraction.

By the end of this chapter, you&apos...

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