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

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Top-level Functions	spacy spacy displacy displacy registry registry Batchers batchers Data & Alignment gold 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, load its config.cfg and use the language and pipeline information to construct 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	Description
name	Model to load, i.e. package name or path. Union[str, Path]
keyword-only
disable	Names of pipeline components to disable. List[str]
config 3	Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. "components.name.value". Union[Dict[str, Any], Config]
RETURNS	A Language object with the loaded model. Language

Essentially, spacy.load() is a convenience wrapper that reads the model's config.cfg, uses the language and pipeline information to construct a Language object, 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()                             #  initialize the language
for name in pipeline:
    nlp.add_pipe(name)                  #  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")   # equivalent to English()
nlp_de = spacy.blank("de")   # equivalent to German()

Name	Description
name	ISO code of the language class to load. str
RETURNS	An empty Language object of the appropriate subclass. Language

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_core_web_sm")
markdown = spacy.info(markdown=True, silent=True)

Name	Description
model	A model, i.e. a package name or path (optional). Optional[str]
keyword-only
markdown	Print information as Markdown. bool
silent	Don't print anything, just return. bool

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	Description
term	Term to explain. str
RETURNS	The explanation, or None if not found in the glossary. Optional[str]

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	Description
RETURNS	Whether the GPU was activated. bool

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	Description
RETURNS	True bool

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

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	Description
docs	Document(s) or span(s) to visualize. Union[Iterable[Union[Doc, Span]], Doc, Span]
style	Visualization style, "dep" or "ent". Defaults to "dep". str
page	Render markup as full HTML page. Defaults to True. bool
minify	Minify HTML markup. Defaults to False. bool
options	Visualizer-specific options, e.g. colors. Dict[str, Any]
manual	Don't parse Doc and instead, expect a dict or list of dicts. See here for formats and examples. Defaults to False. bool
jupyter	Explicitly enable or disable "Jupyter mode" to return markup ready to be rendered in a notebook. Detected automatically if None (default). Optional[bool]
RETURNS	The rendered HTML markup. str

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

Named Entity Visualizer options

Example

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

Name	Description
ents	Entity types to highlight or None for all types (default). Optional[List[str]]
colors	Color overrides. Entity types should be mapped to color names or values. Dict[str, str]
template 2.2	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 for examples. Optional[str]

By default, displaCy comes with colors for all entity types used by spaCy models. 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.

registry

spaCy's function registry extends Thinc's registry and allows you to map strings to functions. You can register functions to create architectures, optimizers, schedules and more, and then refer to them and set their arguments in your config file. Python type hints are used to validate the inputs. See the Thinc docs for details on the registry methods and our helper library catalogue for some background on the concept of function registries. spaCy also uses the function registry for language subclasses, model architecture, lookups and pipeline component factories.

Example

from typing import Iterator
import spacy

@spacy.registry.schedules("waltzing.v1")
def waltzing() -> Iterator[float]:
    i = 0
    while True:
        yield i % 3 + 1
        i += 1

Registry name	Description
architectures	Registry for functions that create model architectures. Can be used to register custom model architectures and reference them in the config.cfg.
assets	Registry for data assets, knowledge bases etc.
batchers	Registry for training and evaluation data batchers.
callbacks	Registry for custom callbacks to modify the nlp object before training.
displacy_colors	Registry for custom color scheme for the displacy NER visualizer. Automatically reads from entry points.
factories	Registry for functions that create pipeline components. Added automatically when you use the @spacy.component decorator and also reads from entry points.
initializers	Registry for functions that create initializers.
languages	Registry for language-specific Language subclasses. Automatically reads from entry points.
layers	Registry for functions that create layers.
lookups	Registry for large lookup tables available via vocab.lookups.
losses	Registry for functions that create losses.
optimizers	Registry for functions that create optimizers.
readers	Registry for training and evaluation data readers like Corpus.
schedules	Registry for functions that create schedules.
tokenizers	Registry for tokenizer factories. Registered functions should return a callback that receives the nlp object and returns a Tokenizer or a custom callable.

spacy-transformers registry

The following registries are added by the spacy-transformers package. See the Transformer API reference and usage docs for details.

Example

import spacy_transformers

@spacy_transformers.registry.annotation_setters("my_annotation_setter.v1")
def configure_custom_annotation_setter():
    def annotation_setter(docs, trf_data) -> None:
       # Set annotations on the docs

    return annotation_sette

Registry name	Description
span_getters	Registry for functions that take a batch of Doc objects and return a list of Span objects to process by the transformer, e.g. sentences.
annotation_setters	Registry for functions that create annotation setters. Annotation setters are functions that take a batch of Doc objects and a FullTransformerBatch and can set additional annotations on the Doc.

Batchers

A data batcher implements a batching strategy that essentially turns a stream of items into a stream of batches, with each batch consisting of one item or a list of items. During training, the models update their weights after processing one batch at a time. Typical batching strategies include presenting the training data as a stream of batches with similar sizes, or with increasing batch sizes. See the Thinc documentation on schedules for a few standard examples.

Instead of using one of the built-in batchers listed here, you can also implement your own, which may or may not use a custom schedule.

batch_by_words.v1

Create minibatches of roughly a given number of words. If any examples are longer than the specified batch length, they will appear in a batch by themselves, or be discarded if discard_oversize is set to True. The argument docs can be a list of strings, Doc objects or Example objects.

Example config

[training.batcher]
@batchers = "batch_by_words.v1"
size = 100
tolerance = 0.2
discard_oversize = false
get_length = null

Name	Description
seqs	The sequences to minibatch. Iterable[Any]
size	The target number of words per batch. Can also be a block referencing a schedule, e.g. compounding. Union[int, Sequence[int]]
tolerance	What percentage of the size to allow batches to exceed. float
discard_oversize	Whether to discard sequences that by themselves exceed the tolerated size. bool
get_length	Optional function that receives a sequence item and returns its length. Defaults to the built-in len() if not set. Optional[CallableAny], int

batch_by_sequence.v1

Example config

[training.batcher]
@batchers = "batch_by_sequence.v1"
size = 32
get_length = null

Create a batcher that creates batches of the specified size.

Name	Description
size	The target number of items per batch. Can also be a block referencing a schedule, e.g. compounding. Union[int, Sequence[int]]
get_length	Optional function that receives a sequence item and returns its length. Defaults to the built-in len() if not set. Optional[CallableAny], int

batch_by_padded.v1

Example config

[training.batcher]
@batchers = "batch_by_padded.v1"
size = 100
buffer = 256
discard_oversize = false
get_length = null

Minibatch a sequence by the size of padded batches that would result, with sequences binned by length within a window. The padded size is defined as the maximum length of sequences within the batch multiplied by the number of sequences in the batch.

Name	Description
size	The largest padded size to batch sequences into. Can also be a block referencing a schedule, e.g. compounding. Union[int, Sequence[int]]
buffer	The number of sequences to accumulate before sorting by length. A larger buffer will result in more even sizing, but if the buffer is very large, the iteration order will be less random, which can result in suboptimal training. int
discard_oversize	Whether to discard sequences that are by themselves longer than the largest padded batch size. bool
get_length	Optional function that receives a sequence item and returns its length. Defaults to the built-in len() if not set. Optional[CallableAny], int

Training data and alignment

gold.biluo_tags_from_offsets

Encode labelled spans into per-token tags, using the BILUO scheme (Begin, In, Last, Unit, Out). Returns a list of strings, describing the tags. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U". The string "-" is used where the entity offsets don't align with the tokenization in the Doc object. The training algorithm will view these as missing values. O denotes a non-entity token. B denotes the beginning of a multi-token entity, I the inside of an entity of three or more tokens, and L the end of an entity of two or more tokens. U denotes a single-token entity.

Example

from spacy.gold import biluo_tags_from_offsets

doc = nlp("I like London.")
entities = [(7, 13, "LOC")]
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "U-LOC", "O"]

Name	Description
doc	The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document. Doc
entities	A sequence of (start, end, label) triples. start and end should be character-offset integers denoting the slice into the original string. List[Tuple[int, int, Union[str, int]]]
RETURNS	A list of strings, describing the BILUO tags. List[str]

gold.offsets_from_biluo_tags

Encode per-token tags following the BILUO scheme into entity offsets.

Example

from spacy.gold import offsets_from_biluo_tags

doc = nlp("I like London.")
tags = ["O", "O", "U-LOC", "O"]
entities = offsets_from_biluo_tags(doc, tags)
assert entities == [(7, 13, "LOC")]

Name	Description
doc	The document that the BILUO tags refer to. Doc
entities	A sequence of BILUO tags with each tag describing one token. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U". List[str]
RETURNS	A sequence of (start, end, label) triples. start and end will be character-offset integers denoting the slice into the original string. List[Tuple[int, int, str]]

gold.spans_from_biluo_tags

Encode per-token tags following the BILUO scheme into Span objects. This can be used to create entity spans from token-based tags, e.g. to overwrite the doc.ents.

Example

from spacy.gold import spans_from_biluo_tags

doc = nlp("I like London.")
tags = ["O", "O", "U-LOC", "O"]
doc.ents = spans_from_biluo_tags(doc, tags)

Name	Description
doc	The document that the BILUO tags refer to. Doc
entities	A sequence of BILUO tags with each tag describing one token. Each tag string will be of the form of either "", "O" or "{action}-{label}", where action is one of "B", "I", "L", "U". List[str]
RETURNS	A sequence of Span objects with added entity labels. List[Span]

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 register it using the @registry.languages decorator.

Example

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

Name	Description
lang	Two-letter language code, e.g. "en". str
RETURNS	The respective subclass. Language

util.lang_class_is_loaded

Check whether a Language subclass is already loaded. Language subclasses 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	Description
name	Two-letter language code, e.g. "en". str
RETURNS	Whether the class has been loaded. bool

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 config.cfg and create a Language object. The model data will then be loaded in via Language.from_disk.

Example

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

Name	Description
name	Package name or model path. str
vocab 3	Optional shared vocab to pass in on initialization. If True (default), a new Vocab object will be created. Union[Vocab, bool].
disable	Names of pipeline components to disable. Iterable[str]
config 3	Config overrides as nested dict or flat dict keyed by section values in dot notation, e.g. "nlp.pipeline". Union[Dict[str, Any], Config]
RETURNS	Language class with the loaded model. Language

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	Description
init_file	Path to model's __init__.py, i.e. __file__. Union[str, Path]
vocab 3	Optional shared vocab to pass in on initialization. If True (default), a new Vocab object will be created. Union[Vocab, bool].
disable	Names of pipeline components to disable. Iterable[str]
config 3	Config overrides as nested dict or flat dict keyed by section values in dot notation, e.g. "nlp.pipeline". Union[Dict[str, Any], Config]
RETURNS	Language class with the loaded model. Language

util.load_config

Load a model's config.cfg from a file path. The config typically includes details about the model pipeline and how its components are created, as well as all training settings and hyperparameters.

Example

config = util.load_config("/path/to/model/config.cfg")
print(config.to_str())

Name	Description
path	Path to the model's config.cfg. Union[str, Path]
overrides	Optional config overrides to replace in loaded config. Can be provided as nested dict, or as flat dict with keys in dot notation, e.g. "nlp.pipeline". Dict[str, Any]
interpolate	Whether to interpolate the config and replace variables like ${paths.train} with their values. Defaults to False. bool
RETURNS	The model's config. Config

util.load_meta

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

Example

meta = util.load_meta("/path/to/model/meta.json")

Name	Description
path	Path to the model's meta.json. Union[str, Path]
RETURNS	The model's meta data. Dict[str, Any]

util.get_installed_models

List all model packages installed in the current environment. This will include any spaCy model that was packaged with spacy package. Under the hood, model packages expose a Python entry point that spaCy can check, without having to load the model.

Example

model_names = util.get_installed_models()

Name	Description
RETURNS	The string names of the models installed in the current environment. List[str]

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

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

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

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	Description
entries	The prefix rules, e.g. lang.punctuation.TOKENIZER_PREFIXES. Iterable[Union[str, Pattern]]
RETURNS	The regex object. to be used for Tokenizer.prefix_search. Pattern

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	Description
entries	The suffix rules, e.g. lang.punctuation.TOKENIZER_SUFFIXES. Iterable[Union[str, Pattern]]
RETURNS	The regex object. to be used for Tokenizer.suffix_search. Pattern

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	Description
entries	The infix rules, e.g. lang.punctuation.TOKENIZER_INFIXES. Iterable[Union[str, Pattern]]
RETURNS	The regex object. to be used for Tokenizer.infix_finditer. Pattern

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:
    nlp.update(batch)

Name	Description
items	The items to batch up. Iterable[Any]
size	int / iterable
YIELDS	The batches.

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	Description
spans	The spans to filter. Iterable[Span]
RETURNS	The filtered spans. List[Span]

util.get_words_and_spaces

Given a list of words and a text, reconstruct the original tokens and return a list of words and spaces that can be used to create a Doc. This can help recover destructive tokenization that didn't preserve any whitespace information.

Example

orig_words = ["Hey", ",", "what", "'s", "up", "?"]
orig_text = "Hey, what's up?"
words, spaces = get_words_and_spaces(orig_words, orig_text)
# ['Hey', ',', 'what', "'s", 'up', '?']
# [False, True, False, True, False, False]

Name	Description
words	The list of words. Iterable[str]
text	The original text. str
RETURNS	A list of words and a list of boolean values indicating whether the word at this position is followed by a space. Tuple[List[str], List[bool]]