//- 💫 DOCS > API > LANGUAGE
include ../_includes/_mixins
p
| Usually you'll load this once per process as #[code nlp] and pass the
| instance around your application. The #[code Language] class is created
| when you call #[+api("spacy#load") #[code spacy.load()]] and contains
| the shared vocabulary and #[+a("/usage/adding-languages") language data],
| optional model data loaded from a #[+a("/models") model package] or
| a path, and a #[+a("/usage/processing-pipelines") 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 #[code Doc] object, modify it and return it.
+h(2, "init") Language.__init__
+tag method
p Initialise a #[code Language] object.
+aside-code("Example").
from spacy.vocab import Vocab
from spacy.language import Language
nlp = Language(Vocab())
from spacy.lang.en import English
nlp = English()
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell
| A #[code Vocab] object. If #[code True], a vocab is created via
| #[code Language.Defaults.create_vocab].
+row
+cell #[code make_doc]
+cell callable
+cell
| A function that takes text and returns a #[code Doc] object.
| Usually a #[code Tokenizer].
+row
+cell #[code meta]
+cell dict
+cell
| Custom meta data for the #[code Language] class. Is written to by
| models to add model meta data.
+row("foot")
+cell returns
+cell #[code Language]
+cell The newly constructed object.
+h(2, "call") Language.__call__
+tag method
p
| Apply the pipeline to some text. The text can span multiple sentences,
| and can contain arbtrary whitespace. Alignment into the original string
| is preserved.
+aside-code("Example").
doc = nlp(u'An example sentence. Another sentence.')
assert (doc[0].text, doc[0].head.tag_) == ('An', 'NN')
+table(["Name", "Type", "Description"])
+row
+cell #[code text]
+cell unicode
+cell The text to be processed.
+row
+cell #[code disable]
+cell list
+cell
| Names of pipeline components to
| #[+a("/usage/processing-pipelines#disabling") disable].
+row("foot")
+cell returns
+cell #[code Doc]
+cell A container for accessing the annotations.
+infobox("Changed in v2.0", "⚠️")
| Pipeline components to prevent from being loaded can now be added as
| a list to #[code disable], instead of specifying one keyword argument
| per component.
+code-wrapper
+code-new doc = nlp(u"I don't want parsed", disable=['parser'])
+code-old doc = nlp(u"I don't want parsed", parse=False)
+h(2, "pipe") Language.pipe
+tag method
p
| Process texts as a stream, and yield #[code Doc] objects in order.
| Supports GIL-free multi-threading.
+aside-code("Example").
texts = [u'One document.', u'...', u'Lots of documents']
for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
assert doc.is_parsed
+table(["Name", "Type", "Description"])
+row
+cell #[code texts]
+cell -
+cell A sequence of unicode objects.
+row
+cell #[code as_tuples]
+cell bool
+cell
| If set to #[code True], inputs should be a sequence of
| #[code (text, context)] tuples. Output will then be a sequence of
| #[code (doc, context)] tuples. Defaults to #[code False].
+row
+cell #[code n_threads]
+cell int
+cell
| The number of worker threads to use. If #[code -1], OpenMP will
| decide how many to use at run time. Default is #[code 2].
+row
+cell #[code batch_size]
+cell int
+cell The number of texts to buffer.
+row
+cell #[code disable]
+cell list
+cell
| Names of pipeline components to
| #[+a("/usage/processing-pipelines#disabling") disable].
+row("foot")
+cell yields
+cell #[code Doc]
+cell Documents in the order of the original text.
+h(2, "update") Language.update
+tag method
p Update the models in the pipeline.
+aside-code("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)
+table(["Name", "Type", "Description"])
+row
+cell #[code docs]
+cell iterable
+cell
| A batch of #[code Doc] objects or unicode. If unicode, a
| #[code Doc] object will be created from the text.
+row
+cell #[code golds]
+cell iterable
+cell
| A batch of #[code GoldParse] objects or dictionaries.
| Dictionaries will be used to create
| #[+api("goldparse") #[code GoldParse]] objects. For the available
| keys and their usage, see
| #[+api("goldparse#init") #[code GoldParse.__init__]].
+row
+cell #[code drop]
+cell float
+cell The dropout rate.
+row
+cell #[code sgd]
+cell callable
+cell An optimizer.
+row("foot")
+cell returns
+cell dict
+cell Results from the update.
+h(2, "begin_training") Language.begin_training
+tag method
p
| Allocate models, pre-process training data and acquire an optimizer.
+aside-code("Example").
optimizer = nlp.begin_training(gold_tuples)
+table(["Name", "Type", "Description"])
+row
+cell #[code gold_tuples]
+cell iterable
+cell Gold-standard training data.
+row
+cell #[code **cfg]
+cell -
+cell Config parameters.
+row("foot")
+cell returns
+cell callable
+cell An optimizer.
+h(2, "use_params") Language.use_params
+tag contextmanager
+tag method
p
| Replace weights of models in the pipeline with those provided in the
| params dictionary. Can be used as a contextmanager, in which case, models
| go back to their original weights after the block.
+aside-code("Example").
with nlp.use_params(optimizer.averages):
nlp.to_disk('/tmp/checkpoint')
+table(["Name", "Type", "Description"])
+row
+cell #[code params]
+cell dict
+cell A dictionary of parameters keyed by model ID.
+row
+cell #[code **cfg]
+cell -
+cell Config parameters.
+h(2, "preprocess_gold") Language.preprocess_gold
+tag method
p
| Can be called before training to pre-process gold data. By default, it
| handles nonprojectivity and adds missing tags to the tag map.
+table(["Name", "Type", "Description"])
+row
+cell #[code docs_golds]
+cell iterable
+cell Tuples of #[code Doc] and #[code GoldParse] objects.
+row("foot")
+cell yields
+cell tuple
+cell Tuples of #[code Doc] and #[code GoldParse] objects.
+h(2, "create_pipe") Language.create_pipe
+tag method
+tag-new(2)
p Create a pipeline component from a factory.
+aside-code("Example").
parser = nlp.create_pipe('parser')
nlp.add_pipe(parser)
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell
| Factory name to look up in
| #[+api("language#class-attributes") #[code Language.factories]].
+row
+cell #[code config]
+cell dict
+cell Configuration parameters to initialise component.
+row("foot")
+cell returns
+cell callable
+cell The pipeline component.
+h(2, "add_pipe") Language.add_pipe
+tag method
+tag-new(2)
p
| Add a component to the processing pipeline. Valid components are
| callables that take a #[code Doc] object, modify it and return it. Only
| one of #[code before], #[code after], #[code first] or #[code last] can
| be set. Default behaviour is #[code last=True].
+aside-code("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)
+table(["Name", "Type", "Description"])
+row
+cell #[code component]
+cell callable
+cell The pipeline component.
+row
+cell #[code name]
+cell unicode
+cell
| Name of pipeline component. Overwrites existing
| #[code component.name] attribute if available. If no #[code name]
| is set and the component exposes no name attribute,
| #[code component.__name__] is used. An error is raised if the
| name already exists in the pipeline.
+row
+cell #[code before]
+cell unicode
+cell Component name to insert component directly before.
+row
+cell #[code after]
+cell unicode
+cell Component name to insert component directly after:
+row
+cell #[code first]
+cell bool
+cell Insert component first / not first in the pipeline.
+row
+cell #[code last]
+cell bool
+cell Insert component last / not last in the pipeline.
+h(2, "has_pipe") Language.has_pipe
+tag method
+tag-new(2)
p
| Check whether a component is present in the pipeline. Equivalent to
| #[code name in nlp.pipe_names].
+aside-code("Example").
nlp.add_pipe(lambda doc: doc, name='component')
assert 'component' in nlp.pipe_names
assert nlp.has_pipe('component')
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the pipeline component to check.
+row("foot")
+cell returns
+cell bool
+cell Whether a component of that name exists in the pipeline.
+h(2, "get_pipe") Language.get_pipe
+tag method
+tag-new(2)
p Get a pipeline component for a given component name.
+aside-code("Example").
parser = nlp.get_pipe('parser')
custom_component = nlp.get_pipe('custom_component')
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the pipeline component to get.
+row("foot")
+cell returns
+cell callable
+cell The pipeline component.
+h(2, "replace_pipe") Language.replace_pipe
+tag method
+tag-new(2)
p Replace a component in the pipeline.
+aside-code("Example").
nlp.replace_pipe('parser', my_custom_parser)
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the component to replace.
+row
+cell #[code component]
+cell callable
+cell The pipeline component to inser.
+h(2, "rename_pipe") Language.rename_pipe
+tag method
+tag-new(2)
p
| 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 #[code name]
| argument on #[+api("language#add_pipe") #[code add_pipe]].
+aside-code("Example").
nlp.rename_pipe('parser', 'spacy_parser')
+table(["Name", "Type", "Description"])
+row
+cell #[code old_name]
+cell unicode
+cell Name of the component to rename.
+row
+cell #[code new_name]
+cell unicode
+cell New name of the component.
+h(2, "remove_pipe") Language.remove_pipe
+tag method
+tag-new(2)
p
| Remove a component from the pipeline. Returns the removed component name
| and component function.
+aside-code("Example").
name, component = nlp.remove_pipe('parser')
assert name == 'parser'
+table(["Name", "Type", "Description"])
+row
+cell #[code name]
+cell unicode
+cell Name of the component to remove.
+row("foot")
+cell returns
+cell tuple
+cell A #[code (name, component)] tuple of the removed component.
+h(2, "disable_pipes") Language.disable_pipes
+tag contextmanager
+tag-new(2)
p
| 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 #[code DisabledPipes] object is returned, that has a
| #[code .restore()] method you can use to undo your changes.
+aside-code("Example").
with nlp.disable_pipes('tagger', 'parser'):
optimizer = nlp.begin_training(gold_tuples)
disabled = nlp.disable_pipes('tagger', 'parser')
optimizer = nlp.begin_training(gold_tuples)
disabled.restore()
+table(["Name", "Type", "Description"])
+row
+cell #[code *disabled]
+cell unicode
+cell Names of pipeline components to disable.
+row("foot")
+cell returns
+cell #[code DisabledPipes]
+cell
| The disabled pipes that can be restored by calling the object's
| #[code .restore()] method.
+h(2, "to_disk") Language.to_disk
+tag method
+tag-new(2)
p
| Save the current state to a directory. If a model is loaded, this will
| #[strong include the model].
+aside-code("Example").
nlp.to_disk('/path/to/models')
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell unicode or #[code Path]
+cell
| A path to a directory, which will be created if it doesn't exist.
| Paths may be either strings or #[code Path]-like objects.
+row
+cell #[code disable]
+cell list
+cell
| Names of pipeline components to
| #[+a("/usage/processing-pipelines#disabling") disable]
| and prevent from being saved.
+h(2, "from_disk") Language.from_disk
+tag method
+tag-new(2)
p
| Loads state from a directory. Modifies the object in place and returns
| it. If the saved #[code Language] object contains a model, the
| #[strong model will be loaded].
+aside-code("Example").
from spacy.language import Language
nlp = Language().from_disk('/path/to/models')
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell unicode or #[code Path]
+cell
| A path to a directory. Paths may be either strings or
| #[code Path]-like objects.
+row
+cell #[code disable]
+cell list
+cell
| Names of pipeline components to
| #[+a("/usage/processing-pipelines#disabling") disable].
+row("foot")
+cell returns
+cell #[code Language]
+cell The modified #[code Language] object.
+infobox("Changed in v2.0", "⚠️")
| As of spaCy v2.0, the #[code save_to_directory] method has been
| renamed to #[code to_disk], to improve consistency across classes.
| Pipeline components to prevent from being loaded can now be added as
| a list to #[code disable], instead of specifying one keyword argument
| per component.
+code-wrapper
+code-new nlp = English().from_disk(disable=['tagger', 'ner'])
+code-old nlp = spacy.load('en', tagger=False, entity=False)
+h(2, "to_bytes") Language.to_bytes
+tag method
p Serialize the current state to a binary string.
+aside-code("Example").
nlp_bytes = nlp.to_bytes()
+table(["Name", "Type", "Description"])
+row
+cell #[code disable]
+cell list
+cell
| Names of pipeline components to
| #[+a("/usage/processing-pipelines#disabling") disable]
| and prevent from being serialized.
+row("foot")
+cell returns
+cell bytes
+cell The serialized form of the #[code Language] object.
+h(2, "from_bytes") Language.from_bytes
+tag method
p Load state from a binary string.
+aside-code("Example").
fron spacy.lang.en import English
nlp_bytes = nlp.to_bytes()
nlp2 = English()
nlp2.from_bytes(nlp_bytes)
+table(["Name", "Type", "Description"])
+row
+cell #[code bytes_data]
+cell bytes
+cell The data to load from.
+row
+cell #[code disable]
+cell list
+cell
| Names of pipeline components to
| #[+a("/usage/processing-pipelines#disabling") disable].
+row("foot")
+cell returns
+cell #[code Language]
+cell The #[code Language] object.
+infobox("Changed in v2.0", "⚠️")
| Pipeline components to prevent from being loaded can now be added as
| a list to #[code disable], instead of specifying one keyword argument
| per component.
+code-wrapper
+code-new nlp = English().from_bytes(bytes, disable=['tagger', 'ner'])
+code-old nlp = English().from_bytes('en', tagger=False, entity=False)
+h(2, "attributes") Attributes
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell A container for the lexical types.
+row
+cell #[code tokenizer]
+cell #[code Tokenizer]
+cell The tokenizer.
+row
+cell #[code make_doc]
+cell #[code lambda text: Doc]
+cell Create a #[code Doc] object from unicode text.
+row
+cell #[code pipeline]
+cell list
+cell
| List of #[code (name, component)] tuples describing the current
| processing pipeline, in order.
+row
+cell #[code pipe_names]
+tag-new(2)
+cell list
+cell List of pipeline component names, in order.
+row
+cell #[code meta]
+cell dict
+cell
| Custom meta data for the Language class. If a model is loaded,
| contains meta data of the model.
+row
+cell #[code path]
+tag-new(2)
+cell #[code Path]
+cell
| Path to the model data directory, if a model is loaded. Otherwise
| #[code None].
+h(2, "class-attributes") Class attributes
+table(["Name", "Type", "Description"])
+row
+cell #[code Defaults]
+cell class
+cell
| Settings, data and factory methods for creating the
| #[code nlp] object and processing pipeline.
+row
+cell #[code lang]
+cell unicode
+cell
| Two-letter language ID, i.e.
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code].
+row
+cell #[code factories]
+tag-new(2)
+cell dict
+cell
| Factories that create pre-defined pipeline components, e.g. the
| tagger, parser or entity recognizer, keyed by their component
| name.