spaCy/website/docs/api/language.md

title	teaser	tag	source
Language	A text-processing pipeline	class	spacy/language.py

Usually you'll load this once per process as nlp and pass the instance around your application. The Language class is created when you call spacy.load() and contains the shared vocabulary and language data, optional model data loaded from a model package or a path, and a processing pipeline containing components like the tagger or parser that are called on a document in order. You can also add your own processing pipeline components that take a Doc object, modify it and return it.

Language.__init__

Initialize a Language object.

Example

from spacy.vocab import Vocab
from spacy.language import Language
nlp = Language(Vocab())

from spacy.lang.en import English
nlp = English()

Name	Type	Description
vocab	Vocab	A Vocab object. If True, a vocab is created via Language.Defaults.create_vocab.
make_doc	callable	A function that takes text and returns a Doc object. Usually a Tokenizer.
meta	dict	Custom meta data for the Language class. Is written to by models to add model meta data.
RETURNS	Language	The newly constructed object.

Language.__call__

Apply the pipeline to some text. The text can span multiple sentences, and can contain arbitrary whitespace. Alignment into the original string is preserved.

Example

doc = nlp("An example sentence. Another sentence.")
assert (doc[0].text, doc[0].head.tag_) == ("An", "NN")

Name	Type	Description
text	str	The text to be processed.
disable	list	Names of pipeline components to disable.
RETURNS	Doc	A container for accessing the annotations.

Pipeline components to prevent from being loaded can now be added as a list to disable, instead of specifying one keyword argument per component.

- doc = nlp("I don't want parsed", parse=False)
+ doc = nlp("I don't want parsed", disable=["parser"])

Language.pipe

Process texts as a stream, and yield Doc objects in order. This is usually more efficient than processing texts one-by-one.

Early versions of spaCy used simple statistical models that could be efficiently multi-threaded, as we were able to entirely release Python's global interpreter lock. The multi-threading was controlled using the n_threads keyword argument to the .pipe method. This keyword argument is now deprecated as of v2.1.0. A new keyword argument, n_process, was introduced to control parallel inference via multiprocessing in v2.2.2.

Example

texts = ["One document.", "...", "Lots of documents"]
for doc in nlp.pipe(texts, batch_size=50):
    assert doc.is_parsed

Name	Type	Description
texts	-	A sequence of unicode objects.
as_tuples	bool	If set to True, inputs should be a sequence of (text, context) tuples. Output will then be a sequence of (doc, context) tuples. Defaults to False.
batch_size	int	The number of texts to buffer.
disable	list	Names of pipeline components to disable.
component_cfg 2.1	dict	Config parameters for specific pipeline components, keyed by component name.
n_process 2.2.2	int	Number of processors to use, only supported in Python 3. Defaults to 1.
YIELDS	Doc	Documents in the order of the original text.

Language.update

Update the models in the pipeline.

Example

for raw_text, entity_offsets in train_data:
    doc = nlp.make_doc(raw_text)
    gold = GoldParse(doc, entities=entity_offsets)
    nlp.update([doc], [gold], drop=0.5, sgd=optimizer)

Name	Type	Description
docs	iterable	A batch of Doc objects or unicode. If unicode, a Doc object will be created from the text.
golds	iterable	A batch of GoldParse objects or dictionaries. Dictionaries will be used to create GoldParse objects. For the available keys and their usage, see GoldParse.__init__.
drop	float	The dropout rate.
sgd	callable	An optimizer.
losses	dict	Dictionary to update with the loss, keyed by pipeline component.
component_cfg 2.1	dict	Config parameters for specific pipeline components, keyed by component name.

Language.evaluate

Evaluate a model's pipeline components.

Example

scorer = nlp.evaluate(docs_golds, verbose=True)
print(scorer.scores)

Name	Type	Description
docs_golds	iterable	Tuples of Doc and GoldParse objects or (text, annotations) of raw text and a dict (see simple training style).
verbose	bool	Print debugging information.
batch_size	int	The batch size to use.
scorer	Scorer	Optional Scorer to use. If not passed in, a new one will be created.
component_cfg 2.1	dict	Config parameters for specific pipeline components, keyed by component name.
RETURNS	Scorer	The scorer containing the evaluation scores.

Language.begin_training

Allocate models, pre-process training data and acquire an optimizer.

Example

optimizer = nlp.begin_training(gold_tuples)

Name	Type	Description
gold_tuples	iterable	Gold-standard training data.
component_cfg 2.1	dict	Config parameters for specific pipeline components, keyed by component name.
**cfg	-	Config parameters (sent to all components).
RETURNS	callable	An optimizer.

Language.use_params

Replace weights of models in the pipeline with those provided in the params dictionary. Can be used as a context manager, in which case, models go back to their original weights after the block.

Example

with nlp.use_params(optimizer.averages):
    nlp.to_disk("/tmp/checkpoint")

Name	Type	Description
params	dict	A dictionary of parameters keyed by model ID.
**cfg	-	Config parameters.

Language.preprocess_gold

Can be called before training to pre-process gold data. By default, it handles nonprojectivity and adds missing tags to the tag map.

Name	Type	Description
docs_golds	iterable	Tuples of Doc and GoldParse objects.
YIELDS	tuple	Tuples of Doc and GoldParse objects.

Language.create_pipe

Create a pipeline component from a factory.

Example

parser = nlp.create_pipe("parser")
nlp.add_pipe(parser)

Name	Type	Description
name	str	Factory name to look up in Language.factories.
config	dict	Configuration parameters to initialize component.
RETURNS	callable	The pipeline component.

Language.add_pipe

Add a component to the processing pipeline. Valid components are callables that take a Doc object, modify it and return it. Only one of before, after, first or last can be set. Default behavior is last=True.

Example

def component(doc):
    # modify Doc and return it return doc

nlp.add_pipe(component, before="ner")
nlp.add_pipe(component, name="custom_name", last=True)

Name	Type	Description
component	callable	The pipeline component.
name	str	Name of pipeline component. Overwrites existing component.name attribute if available. If no name is set and the component exposes no name attribute, component.__name__ is used. An error is raised if the name already exists in the pipeline.
before	str	Component name to insert component directly before.
after	str	Component name to insert component directly after:
first	bool	Insert component first / not first in the pipeline.
last	bool	Insert component last / not last in the pipeline.

Language.has_pipe

Check whether a component is present in the pipeline. Equivalent to name in nlp.pipe_names.

Example

nlp.add_pipe(lambda doc: doc, name="component")
assert "component" in nlp.pipe_names
assert nlp.has_pipe("component")

Name	Type	Description
name	str	Name of the pipeline component to check.
RETURNS	bool	Whether a component of that name exists in the pipeline.

Language.get_pipe

Get a pipeline component for a given component name.

Example

parser = nlp.get_pipe("parser")
custom_component = nlp.get_pipe("custom_component")

Name	Type	Description
name	str	Name of the pipeline component to get.
RETURNS	callable	The pipeline component.

Language.replace_pipe

Replace a component in the pipeline.

Example

nlp.replace_pipe("parser", my_custom_parser)

Name	Type	Description
name	str	Name of the component to replace.
component	callable	The pipeline component to insert.

Language.rename_pipe

Rename a component in the pipeline. Useful to create custom names for pre-defined and pre-loaded components. To change the default name of a component added to the pipeline, you can also use the name argument on add_pipe.

Example

nlp.rename_pipe("parser", "spacy_parser")

Name	Type	Description
old_name	str	Name of the component to rename.
new_name	str	New name of the component.

Language.remove_pipe

Remove a component from the pipeline. Returns the removed component name and component function.

Example

name, component = nlp.remove_pipe("parser")
assert name == "parser"

Name	Type	Description
name	str	Name of the component to remove.
RETURNS	tuple	A (name, component) tuple of the removed component.

Language.select_pipes

Disable one or more pipeline components. If used as a context manager, the pipeline will be restored to the initial state at the end of the block. Otherwise, a DisabledPipes object is returned, that has a .restore() method you can use to undo your changes.

You can specify either disable (as a list or string), or enable. In the latter case, all components not in the enable list, will be disabled.

Example

# New API as of v3.0
with nlp.select_pipes(disable=["tagger", "parser"]):
   nlp.begin_training()

with nlp.select_pipes(enable="ner"):
    nlp.begin_training()

disabled = nlp.select_pipes(disable=["tagger", "parser"])
nlp.begin_training()
disabled.restore()

Name	Type	Description
disable	list	Names of pipeline components to disable.
disable	str	Name of pipeline component to disable.
enable	list	Names of pipeline components that will not be disabled.
enable	str	Name of pipeline component that will not be disabled.
RETURNS	DisabledPipes	The disabled pipes that can be restored by calling the object's .restore() method.

As of spaCy v3.0, the disable_pipes method has been renamed to select_pipes:

- nlp.disable_pipes(["tagger", "parser"])
+ nlp.select_pipes(disable=["tagger", "parser"])

Language.to_disk

Save the current state to a directory. If a model is loaded, this will include the model.

Example

nlp.to_disk("/path/to/models")

Name	Type	Description
path	str / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.
exclude	list	Names of pipeline components or serialization fields to exclude.

Language.from_disk

Loads state from a directory. Modifies the object in place and returns it. If the saved Language object contains a model, the model will be loaded. Note that this method is commonly used via the subclasses like English or German to make language-specific functionality like the lexical attribute getters available to the loaded object.

Example

from spacy.language import Language
nlp = Language().from_disk("/path/to/model")

# using language-specific subclass
from spacy.lang.en import English
nlp = English().from_disk("/path/to/en_model")

Name	Type	Description
path	str / Path	A path to a directory. Paths may be either strings or Path-like objects.
exclude	list	Names of pipeline components or serialization fields to exclude.
RETURNS	Language	The modified Language object.

As of spaCy v2.0, the save_to_directory method has been renamed to to_disk, to improve consistency across classes. Pipeline components to prevent from being loaded can now be added as a list to disable (v2.0) or exclude (v2.1), instead of specifying one keyword argument per component.

- nlp = spacy.load("en", tagger=False, entity=False)
+ nlp = English().from_disk("/model", exclude=["tagger", "ner"])

Language.to_bytes

Serialize the current state to a binary string.

Example

nlp_bytes = nlp.to_bytes()

Name	Type	Description
exclude	list	Names of pipeline components or serialization fields to exclude.
RETURNS	bytes	The serialized form of the Language object.

Language.from_bytes

Load state from a binary string. Note that this method is commonly used via the subclasses like English or German to make language-specific functionality like the lexical attribute getters available to the loaded object.

Example

from spacy.lang.en import English
nlp_bytes = nlp.to_bytes()
nlp2 = English()
nlp2.from_bytes(nlp_bytes)

Name	Type	Description
bytes_data	bytes	The data to load from.
exclude	list	Names of pipeline components or serialization fields to exclude.
RETURNS	Language	The Language object.

Pipeline components to prevent from being loaded can now be added as a list to disable (v2.0) or exclude (v2.1), instead of specifying one keyword argument per component.

- nlp = English().from_bytes(bytes, tagger=False, entity=False)
+ nlp = English().from_bytes(bytes, exclude=["tagger", "ner"])

Attributes

Name	Type	Description
vocab	Vocab	A container for the lexical types.
tokenizer	Tokenizer	The tokenizer.
make_doc	callable	Callable that takes a unicode text and returns a Doc.
pipeline	list	List of (name, component) tuples describing the current processing pipeline, in order.
pipe_names 2	list	List of pipeline component names, in order.
pipe_labels 2.2	dict	List of labels set by the pipeline components, if available, keyed by component name.
meta	dict	Custom meta data for the Language class. If a model is loaded, contains meta data of the model.
path 2	Path	Path to the model data directory, if a model is loaded. Otherwise None.

Class attributes

Name	Type	Description
Defaults	class	Settings, data and factory methods for creating the nlp object and processing pipeline.
lang	str	Two-letter language ID, i.e. ISO code.
factories 2	dict	Factories that create pre-defined pipeline components, e.g. the tagger, parser or entity recognizer, keyed by their component name.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = nlp.to_bytes(exclude=["tokenizer", "vocab"])
nlp.from_disk("./model-data", exclude=["ner"])

Name	Description
vocab	The shared Vocab.
tokenizer	Tokenization rules and exceptions.
meta	The meta data, available as Language.meta.
...	String names of pipeline components, e.g. "ner".