spaCy/website/docs/api/top-level.md

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Top-level Functions	spacy spacy displacy displacy Utility Functions util

spaCy

spacy.load

Load a model using the name of an installed model package, a string path or a Path-like object. spaCy will try resolving the load argument in this order. If a model is loaded from a model name, spaCy will assume it's a Python package and import it and call the model's own load() method. If a model is loaded from a path, spaCy will assume it's a data directory, read the language and pipeline settings off the meta.json and initialize the Language class. The data will be loaded in via Language.from_disk.

Example

nlp = spacy.load("en_core_web_sm") # package
nlp = spacy.load("/path/to/en") # string path
nlp = spacy.load(Path("/path/to/en")) # pathlib Path

nlp = spacy.load("en_core_web_sm", disable=["parser", "tagger"])

Name	Type	Description
name	str / Path	Model to load, i.e. package name or path.
disable	list	Names of pipeline components to disable.
RETURNS	Language	A Language object with the loaded model.

Essentially, spacy.load() is a convenience wrapper that reads the language ID and pipeline components from a model's meta.json, initializes the Language class, loads in the model data and returns it.

### Abstract example
cls = util.get_lang_class(lang)         #  get language for ID, e.g. 'en'
nlp = cls()                             #  initialise the language
for name in pipeline: component = nlp.create_pipe(name)   #  create each pipeline component nlp.add_pipe(component)             #  add component to pipeline
nlp.from_disk(model_data_path)          #  load in model data

spacy.blank

Create a blank model of a given language class. This function is the twin of spacy.load().

Example

nlp_en = spacy.blank("en")
nlp_de = spacy.blank("de")

Name	Type	Description
name	str	ISO code of the language class to load.
disable	list	Names of pipeline components to disable.
RETURNS	Language	An empty Language object of the appropriate subclass.

spacy.info

The same as the info command. Pretty-print information about your installation, models and local setup from within spaCy. To get the model meta data as a dictionary instead, you can use the meta attribute on your nlp object with a loaded model, e.g. nlp.meta.

Example

spacy.info()
spacy.info("en")
spacy.info("de", markdown=True)

Name	Type	Description
model	str	A model, i.e. a package name or path (optional).
markdown	bool	Print information as Markdown.

spacy.explain

Get a description for a given POS tag, dependency label or entity type. For a list of available terms, see glossary.py.

Example

spacy.explain("NORP")
# Nationalities or religious or political groups

doc = nlp("Hello world")
for word in doc:
   print(word.text, word.tag_, spacy.explain(word.tag_))
# Hello UH interjection
# world NN noun, singular or mass

Name	Type	Description
term	str	Term to explain.
RETURNS	str	The explanation, or None if not found in the glossary.

spacy.prefer_gpu

Allocate data and perform operations on GPU, if available. If data has already been allocated on CPU, it will not be moved. Ideally, this function should be called right after importing spaCy and before loading any models.

Example

import spacy
activated = spacy.prefer_gpu()
nlp = spacy.load("en_core_web_sm")

Name	Type	Description
RETURNS	bool	Whether the GPU was activated.

spacy.require_gpu

Allocate data and perform operations on GPU. Will raise an error if no GPU is available. If data has already been allocated on CPU, it will not be moved. Ideally, this function should be called right after importing spaCy and before loading any models.

Example

import spacy
spacy.require_gpu()
nlp = spacy.load("en_core_web_sm")

Name	Type	Description
RETURNS	bool	True

displaCy

As of v2.0, spaCy comes with a built-in visualization suite. For more info and examples, see the usage guide on visualizing spaCy.

displacy.serve

Serve a dependency parse tree or named entity visualization to view it in your browser. Will run a simple web server.

Example

import spacy
from spacy import displacy
nlp = spacy.load("en_core_web_sm")
doc1 = nlp("This is a sentence.")
doc2 = nlp("This is another sentence.")
displacy.serve([doc1, doc2], style="dep")

Name	Type	Description	Default
docs	list, Doc, Span	Document(s) to visualize.
style	str	Visualization style, 'dep' or 'ent'.	'dep'
page	bool	Render markup as full HTML page.	True
minify	bool	Minify HTML markup.	False
options	dict	Visualizer-specific options, e.g. colors.	{}
manual	bool	Don't parse Doc and instead, expect a dict or list of dicts. See here for formats and examples.	False
port	int	Port to serve visualization.	5000
host	str	Host to serve visualization.	'0.0.0.0'

displacy.render

Render a dependency parse tree or named entity visualization.

Example

import spacy
from spacy import displacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a sentence.")
html = displacy.render(doc, style="dep")

Name	Type	Description	Default
docs	list, Doc, Span	Document(s) to visualize.
style	str	Visualization style, 'dep' or 'ent'.	'dep'
page	bool	Render markup as full HTML page.	False
minify	bool	Minify HTML markup.	False
jupyter	bool	Explicitly enable or disable "Jupyter mode" to return markup ready to be rendered in a notebook. Detected automatically if None.	None
options	dict	Visualizer-specific options, e.g. colors.	{}
manual	bool	Don't parse Doc and instead, expect a dict or list of dicts. See here for formats and examples.	False
RETURNS	str	Rendered HTML markup.

Visualizer options

The options argument lets you specify additional settings for each visualizer. If a setting is not present in the options, the default value will be used.

Dependency Visualizer options

Example

options = {"compact": True, "color": "blue"}
displacy.serve(doc, style="dep", options=options)

Name	Type	Description	Default
fine_grained	bool	Use fine-grained part-of-speech tags (Token.tag_) instead of coarse-grained tags (Token.pos_).	False
add_lemma 2.2.4	bool	Print the lemma's in a separate row below the token texts.	False
collapse_punct	bool	Attach punctuation to tokens. Can make the parse more readable, as it prevents long arcs to attach punctuation.	True
collapse_phrases	bool	Merge noun phrases into one token.	False
compact	bool	"Compact mode" with square arrows that takes up less space.	False
color	str	Text color (HEX, RGB or color names).	'#000000'
bg	str	Background color (HEX, RGB or color names).	'#ffffff'
font	str	Font name or font family for all text.	'Arial'
offset_x	int	Spacing on left side of the SVG in px.	50
arrow_stroke	int	Width of arrow path in px.	2
arrow_width	int	Width of arrow head in px.	10 / 8 (compact)
arrow_spacing	int	Spacing between arrows in px to avoid overlaps.	20 / 12 (compact)
word_spacing	int	Vertical spacing between words and arcs in px.	45
distance	int	Distance between words in px.	175 / 150 (compact)

Named Entity Visualizer options

Example

options = {"ents": ["PERSON", "ORG", "PRODUCT"],
           "colors": {"ORG": "yellow"}}
displacy.serve(doc, style="ent", options=options)

Name	Type	Description	Default
ents	list	Entity types to highlight (None for all types).	None
colors	dict	Color overrides. Entity types in uppercase should be mapped to color names or values.	{}
template 2.2	str	Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use {bg}, {text} and {label}.	see templates.py

By default, displaCy comes with colors for all entity types supported by spaCy. If you're using custom entity types, you can use the colors setting to add your own colors for them. Your application or model package can also expose a spacy_displacy_colors entry point to add custom labels and their colors automatically.

Utility functions

spaCy comes with a small collection of utility functions located in spacy/util.py. Because utility functions are mostly intended for internal use within spaCy, their behavior may change with future releases. The functions documented on this page should be safe to use and we'll try to ensure backwards compatibility. However, we recommend having additional tests in place if your application depends on any of spaCy's utilities.

util.get_lang_class

Import and load a Language class. Allows lazy-loading language data and importing languages using the two-letter language code. To add a language code for a custom language class, you can use the set_lang_class helper.

Example

for lang_id in ["en", "de"]:
    lang_class = util.get_lang_class(lang_id)
    lang = lang_class()
    tokenizer = lang.Defaults.create_tokenizer()

Name	Type	Description
lang	str	Two-letter language code, e.g. 'en'.
RETURNS	Language	Language class.

util.set_lang_class

Set a custom Language class name that can be loaded via get_lang_class. If your model uses a custom language, this is required so that spaCy can load the correct class from the two-letter language code.

Example

from spacy.lang.xy import CustomLanguage

util.set_lang_class('xy', CustomLanguage)
lang_class = util.get_lang_class('xy')
nlp = lang_class()

Name	Type	Description
name	str	Two-letter language code, e.g. 'en'.
cls	Language	The language class, e.g. English.

util.lang_class_is_loaded

Check whether a Language class is already loaded. Language classes are loaded lazily, to avoid expensive setup code associated with the language data.

Example

lang_cls = util.get_lang_class("en")
assert util.lang_class_is_loaded("en") is True
assert util.lang_class_is_loaded("de") is False

Name	Type	Description
name	str	Two-letter language code, e.g. 'en'.
RETURNS	bool	Whether the class has been loaded.

util.load_model

Load a model from a package or data path. If called with a package name, spaCy will assume the model is a Python package and import and call its load() method. If called with a path, spaCy will assume it's a data directory, read the language and pipeline settings from the meta.json and initialize a Language class. The model data will then be loaded in via Language.from_disk().

Example

nlp = util.load_model("en")
nlp = util.load_model("en_core_web_sm", disable=["ner"])
nlp = util.load_model("/path/to/data")

Name	Type	Description
name	str	Package name or model path.
**overrides	-	Specific overrides, like pipeline components to disable.
RETURNS	Language	Language class with the loaded model.

util.load_model_from_path

Load a model from a data directory path. Creates the Language class and pipeline based on the directory's meta.json and then calls from_disk() with the path. This function also makes it easy to test a new model that you haven't packaged yet.

Example

nlp = load_model_from_path("/path/to/data")

Name	Type	Description
model_path	str	Path to model data directory.
meta	dict	Model meta data. If False, spaCy will try to load the meta from a meta.json in the same directory.
**overrides	-	Specific overrides, like pipeline components to disable.
RETURNS	Language	Language class with the loaded model.

util.load_model_from_init_py

A helper function to use in the load() method of a model package's __init__.py.

Example

from spacy.util import load_model_from_init_py

def load(**overrides):
    return load_model_from_init_py(__file__, **overrides)

Name	Type	Description
init_file	str	Path to model's __init__.py, i.e. __file__.
**overrides	-	Specific overrides, like pipeline components to disable.
RETURNS	Language	Language class with the loaded model.

util.get_model_meta

Get a model's meta.json from a directory path and validate its contents.

Example

meta = util.get_model_meta("/path/to/model")

Name	Type	Description
path	str / Path	Path to model directory.
RETURNS	dict	The model's meta data.

util.is_package

Check if string maps to a package installed via pip. Mainly used to validate model packages.

Example

util.is_package("en_core_web_sm") # True
util.is_package("xyz") # False

Name	Type	Description
name	str	Name of package.
RETURNS	bool	True if installed package, False if not.

util.get_package_path

Get path to an installed package. Mainly used to resolve the location of model packages. Currently imports the package to find its path.

Example

util.get_package_path("en_core_web_sm")
# /usr/lib/python3.6/site-packages/en_core_web_sm

Name	Type	Description
package_name	str	Name of installed package.
RETURNS	Path	Path to model package directory.

util.is_in_jupyter

Check if user is running spaCy from a Jupyter notebook by detecting the IPython kernel. Mainly used for the displacy visualizer.

Example

html = "<h1>Hello world!</h1>"
if util.is_in_jupyter():
    from IPython.core.display import display, HTML
    display(HTML(html))

Name	Type	Description
RETURNS	bool	True if in Jupyter, False if not.

util.update_exc

Update, validate and overwrite tokenizer exceptions. Used to combine global exceptions with custom, language-specific exceptions. Will raise an error if key doesn't match ORTH values.

Example

BASE =  {"a.": [{ORTH: "a."}], ":)": [{ORTH: ":)"}]}
NEW = {"a.": [{ORTH: "a.", NORM: "all"}]}
exceptions = util.update_exc(BASE, NEW)
# {"a.": [{ORTH: "a.", NORM: "all"}], ":)": [{ORTH: ":)"}]}

Name	Type	Description
base_exceptions	dict	Base tokenizer exceptions.
*addition_dicts	dicts	Exception dictionaries to add to the base exceptions, in order.
RETURNS	dict	Combined tokenizer exceptions.

util.compile_prefix_regex

Compile a sequence of prefix rules into a regex object.

Example

prefixes = ("§", "%", "=", r"\+")
prefix_regex = util.compile_prefix_regex(prefixes)
nlp.tokenizer.prefix_search = prefix_regex.search

Name	Type	Description
entries	tuple	The prefix rules, e.g. lang.punctuation.TOKENIZER_PREFIXES.
RETURNS	regex	The regex object. to be used for Tokenizer.prefix_search.

util.compile_suffix_regex

Compile a sequence of suffix rules into a regex object.

Example

suffixes = ("'s", "'S", r"(?<=[0-9])\+")
suffix_regex = util.compile_suffix_regex(suffixes)
nlp.tokenizer.suffix_search = suffix_regex.search

Name	Type	Description
entries	tuple	The suffix rules, e.g. lang.punctuation.TOKENIZER_SUFFIXES.
RETURNS	regex	The regex object. to be used for Tokenizer.suffix_search.

util.compile_infix_regex

Compile a sequence of infix rules into a regex object.

Example

infixes = ("…", "-", "—", r"(?<=[0-9])[+\-\*^](?=[0-9-])")
infix_regex = util.compile_infix_regex(infixes)
nlp.tokenizer.infix_finditer = infix_regex.finditer

Name	Type	Description
entries	tuple	The infix rules, e.g. lang.punctuation.TOKENIZER_INFIXES.
RETURNS	regex	The regex object. to be used for Tokenizer.infix_finditer.

util.minibatch

Iterate over batches of items. size may be an iterator, so that batch-size can vary on each step.

Example

batches = minibatch(train_data)
for batch in batches:
    texts, annotations = zip(*batch)
    nlp.update(texts, annotations)

Name	Type	Description
items	iterable	The items to batch up.
size	int / iterable	The batch size(s). Use util.compounding or util.decaying or for an infinite series of compounding or decaying values.
YIELDS	list	The batches.

util.compounding

Yield an infinite series of compounding values. Each time the generator is called, a value is produced by multiplying the previous value by the compound rate.

Example

sizes = compounding(1., 10., 1.5)
assert next(sizes) == 1.
assert next(sizes) == 1. * 1.5
assert next(sizes) == 1.5 * 1.5

Name	Type	Description
start	int / float	The first value.
stop	int / float	The maximum value.
compound	int / float	The compounding factor.
YIELDS	int	Compounding values.

util.decaying

Yield an infinite series of linearly decaying values.

Example

sizes = decaying(10., 1., 0.001)
assert next(sizes) == 10.
assert next(sizes) == 10. - 0.001
assert next(sizes) == 9.999 - 0.001

Name	Type	Description
start	int / float	The first value.
end	int / float	The maximum value.
decay	int / float	The decaying factor.
YIELDS	int	The decaying values.

util.itershuffle

Shuffle an iterator. This works by holding bufsize items back and yielding them sometime later. Obviously, this is not unbiased – but should be good enough for batching. Larger bufsize means less bias.

Example

values = range(1000)
shuffled = itershuffle(values)

Name	Type	Description
iterable	iterable	Iterator to shuffle.
bufsize	int	Items to hold back (default: 1000).
YIELDS	iterable	The shuffled iterator.

util.filter_spans

Filter a sequence of Span objects and remove duplicates or overlaps. Useful for creating named entities (where one token can only be part of one entity) or when merging spans with Retokenizer.merge. When spans overlap, the (first) longest span is preferred over shorter spans.

Example

doc = nlp("This is a sentence.")
spans = [doc[0:2], doc[0:2], doc[0:4]]
filtered = filter_spans(spans)

Name	Type	Description
spans	iterable	The spans to filter.
RETURNS	list	The filtered spans.