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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
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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

Using third-party word vectors

We can also use third-party word vectors within spaCy. In this section, we'll learn how to import a third-party word vector package into spaCy. We'll use fastText's subword-based pretrained vectors from the Facebook AI. You can view the list of all the available English pretrained vectors at https://fasttext.cc/docs/en/english-vectors.html.

The name of the package identifies the vector's dimension, the vocabulary size, and the corpus genre that the vectors will be trained on. For instance, wiki-news-300d-1M-subword.vec.zip indicates that it contains 1 million 300-dimensional word vectors that have been trained on a Wikipedia corpus.

Let's start downloading the vectors:

  1. In your terminal, type the following command. Alternatively, you can copy and paste the URL into your browser and the download should start:
    $ wget https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.vec.zip

    The preceding...

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