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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

Transformers and spaCy

spaCy v3.0 was released with great new features and components. The most exciting new feature is undoubtedly transformer-based pipelines. The new transformer-based pipelines bring spaCy's accuracy to the state of the art. Integrating transformers into the spaCy NLP pipeline introduced one more pipeline component called Transformer. This component allows us to use all HuggingFace models with spaCy pipelines. If we recall from Chapter 2, Core Operations with spaCy, this is what the spaCy NLP pipeline looks like without transformers:

Figure 9.11 – Vector-based spaCy pipeline components

With the release of v3.0, v2 style spaCy models are still supported and transformer-based models are introduced. A transformer-based pipeline component looks like the following:

Figure 9.12 – Transformed-based spaCy pipeline components

For each supported language, transformer-based models and v2 style models are...

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