Book Image

Mastering spaCy

By : Duygu Altınok
5 (1)
Book Image

Mastering spaCy

5 (1)
By: Duygu Altınok

Overview of this book

spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications. You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results. By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.
Table of Contents (15 chapters)
1
Section 1: Getting Started with spaCy
4
Section 2: spaCy Features
9
Section 3: Machine Learning with spaCy

To get the most out of this book

First of all, you'll need Python 3 installed and working on your system. Code examples are tested with spaCy v3.0, however, most of the code is compatible with spaCy v2.3 due to backwards compatibility. For the helper libraries such as scikit-learn, pandas, NumPy, and matplotlib, the latest versions available on pip will work. We use TensorFlow, transformers, and helper libraries starting with Chapter 7, Customizing spaCy Models, so you can install these libraries by the time you reach Chapter 7.

We used Jupyter notebooks from time to time. You can view the notebooks on the book's GitHub page. If you want to work with Jupyter notebooks, that's great; you can install Jupyter via pip. If you don't want to, you can still copy and paste the code into the Python shell and make the code work.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.