Chapter 3: Processing Pipelines

This chapter will show you everything you need to know about spaCy's processing pipeline. You'll learn what goes on under the hood when you process a text, how to write your own components and add them to the pipeline, and how to use custom attributes to add your own metadata to the documents, spans and tokens.

1Processing pipelines

2What happens when you call nlp?

3Inspecting the pipeline

4Custom pipeline components

5Use cases for custom components

6Simple components

7Complex components

8Extension attributes

9Setting extension attributes (1)

10Setting extension attributes (2)

11Entities and extensions

12Components with extensions

13Scaling and performance

14Processing streams

15Processing data with context

16Selective processing

About this course

spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

About me

I'm Ines, one of the core developers of spaCy and the co-founder of Explosion. I specialize in modern developer tools for AI, Machine Learning and NLP. I also really love building stuff for the web.