Scaling and performance

Advanced NLP with spaCy

Ines Montani

spaCy core developer

Processing large volumes of text

Use nlp.pipe method
Processes texts as a stream, yields Doc objects
Much faster than calling nlp on each text

BAD:

docs = [nlp(text) for text in LOTS_OF_TEXTS]

GOOD:

docs = list(nlp.pipe(LOTS_OF_TEXTS))

Passing in context (1)

Setting as_tuples=True on nlp.pipe lets you pass in (text, context) tuples
Yields (doc, context) tuples
Useful for associating metadata with the doc

data = [
    ('This is a text', {'id': 1, 'page_number': 15}),
    ('And another text', {'id': 2, 'page_number': 16}),
]

for doc, context in nlp.pipe(data, as_tuples=True):
    print(doc.text, context['page_number'])

This is a text 15
And another text 16

Passing in context (2)

from spacy.tokens import Doc

Doc.set_extension('id', default=None)
Doc.set_extension('page_number', default=None)

data = [
    ('This is a text', {'id': 1, 'page_number': 15}),
    ('And another text', {'id': 2, 'page_number': 16}),
]

for doc, context in nlp.pipe(data, as_tuples=True):
    doc._.id = context['id']
    doc._.page_number = context['page_number']

Using only the tokenizer

Illustration of the spaCy pipeline

don't run the whole pipeline!

Using only the tokenizer (2)

Use nlp.make_doc to turn a text in to a Doc object

BAD:

doc = nlp("Hello world")

GOOD:

doc = nlp.make_doc("Hello world!")

Disabling pipeline components

Use nlp.disable_pipes to temporarily disable one or more pipes

# Disable tagger and parser
with nlp.disable_pipes('tagger', 'parser'):
    # Process the text and print the entities
    doc = nlp(text)
    print(doc.ents)

restores them after the with block
only runs the remaining components

Let's practice!

Advanced NLP with spaCy