spaCy is an open-source library used for advanced Natural Language Processing (NLP) in Python. spaCy is primarily used in the production environment for building applications that can process and understand large volumes of text. It is a fast library with an API that accesses the methods and properties governed by machine learning models.
spaCy is a free and open-source library for performing NLP tasks efficiently. It is written in the Python and Cython programming languages. While NLTK is used mainly in teaching and research, spaCy is focused on production-ready systems. The primary use of spaCy is for actual work–for building real products and deriving accurate insights, as you will learn throughout this spaCy guide. Some of the benefits of this library is that it is easy to install and the API is easy to use and very productive. spaCy also excels at processing large-scale information and extraction tasks. Since its release, spaCy has set itself as an industry standard with a large ecosystem comprising plugins, integration with machine learning stack, and custom components and workflows.
In this free-to-download spaCy guide, we walk you through some core aspects of spaCy, including a general overview, jobs that use spaCy, key terminology, and algorithms that you need to get started.