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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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Toc

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

Technical requirements

The code in the sections Training the spaCy text classifier and Sentiment analysis with spaCy is spaCy v3.0 compatible.

The section Text classification with spaCy and Keras requires the following Python libraries:

  • TensorFlow >=2.2.0
  • NumPy
  • pandas
  • Matplotlib

You can install the latest version of these libraries with pip as follows:

pip install tensorflow
pip install numpy
pip install pandas
pip install matplotlib

We also use Jupyter notebooks in the last two sections. You can follow the instructions on the Jupyter website (https://jupyter.org/install) to install the Jupyter notebook onto your system. If you don't want to use notebooks, you can copy-paste code as Python code as well.

You can find the chapter code and data files in the book's GitHub repository at https://github.com/PacktPublishing/Mastering-spaCy/tree/main/Chapter08.

Let's get started with spaCy's text classifier component first,...

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