spaCy is a free, open-source Python library that provides advanced capabilities to conduct natural language processing (NLP) on large volumes of text at high speed. It helps you build models and production applications that can underpin document analysis, chatbot capabilities, and all other forms of text analysis.
The two principal authors for spaCy, Matthew Honnibal and Ines Montani, launched the project in 2015. The spaCy framework—along with a growing set of plug-ins and other integrations—provides features for a wide range of natural language tasks. It’s become one of the most widely used natural language libraries in Python for industry use cases, and has quite a large community—and with that, much support for commercialization of research advances as this area continues to evolve rapidly.
The spaCy Universe offers deep-dives into particular use cases and to see how this field is evolving. Some selections from this “universe” include:
The latest release, spaCy 3.0, brings many improvements to help build, configure and maintain NLP models, including:
These features combine to make spaCy better than ever at processing large volumes of text and tuning configurations to match specific use cases in a way that provides better accuracy.