Custom pipeline components

Advanced NLP with spaCy

Ines Montani

spaCy core developer

Why custom components?

Illustration of the spaCy pipeline

Make a function execute automatically when you call nlp
Add your own metadata to documents and tokens
Updating built-in attributes like doc.ents

Anatomy of a component (1)

Function that takes a doc, modifies it and returns it
Can be added using the nlp.add_pipe method

def custom_component(doc):
    # Do something to the doc here
    return doc

nlp.add_pipe(custom_component)

Anatomy of a component (2)

def custom_component(doc):
    # Do something to the doc here
    return doc

nlp.add_pipe(custom_component)

Argument	Description	Example
`last`	If `True`, add last	`nlp.add_pipe(component, last=True)`
`first`	If `True`, add first	`nlp.add_pipe(component, first=True)`
`before`	Add before component	`nlp.add_pipe(component, before='ner')`
`after`	Add after component	`nlp.add_pipe(component, after='tagger')`

Example: a simple component (1)

# Create the nlp object
nlp = spacy.load('en_core_web_sm')

# Define a custom component
def custom_component(doc):

    # Print the doc's length
    print('Doc length:' len(doc))

    # Return the doc object
    return doc

# Add the component first in the pipeline
nlp.add_pipe(custom_component, first=True)

# Print the pipeline component names
print('Pipeline:', nlp.pipe_names)

Pipeline: ['custom_component', 'tagger', 'parser', 'ner']

Example: a simple component (2)

# Create the nlp object
nlp = spacy.load('en_core_web_sm')

# Define a custom component
def custom_component(doc):

    # Print the doc's length
    print('Doc length:' len(doc))

    # Return the doc object
    return doc

# Add the component first in the pipeline
nlp.add_pipe(custom_component, first=True)

# Process a text
doc = nlp("Hello world!")

Doc length: 3

Let's practice!

Advanced NLP with spaCy