//- 💫 DOCS > API > LANGUAGE
include ../../_includes/_mixins
p
| A text-processing pipeline. Usually you'll load this once per process,
| and pass the instance around your application.
+h(2, "init") Language.__init__
+tag method
p Initialise a #[code Language] object.
+aside-code("Example").
from spacy.language import Language
nlp = Language(pipeline=['token_vectors', 'tags',
'dependencies'])
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 pipeline]
+cell list
+cell
| A list of annotation processes or IDs of annotation, processes,
| e.g. a #[code Tagger] object, or #[code 'tagger']. IDs are looked
| up in #[code Language.Defaults.factories].
+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.
+footrow
+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 **disabled]
+cell -
+cell Elements of the pipeline that should not be run.
+footrow
+cell returns
+cell #[code Doc]
+cell A container for accessing the annotations.
+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 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.
+footrow
+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").
with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
for epoch in trainer.epochs(gold):
for docs, golds in epoch:
state = nlp.update(docs, golds, sgd=optimizer)
+table(["Name", "Type", "Description"])
+row
+cell #[code docs]
+cell iterable
+cell A batch of #[code Doc] objects.
+row
+cell #[code golds]
+cell iterable
+cell A batch of #[code GoldParse] objects.
+row
+cell #[code drop]
+cell float
+cell The dropout rate.
+row
+cell #[code sgd]
+cell callable
+cell An optimizer.
+footrow
+cell returns
+cell dict
+cell Results from the update.
+h(2, "begin_training") Language.begin_training
+tag contextmanager
p
| Allocate models, pre-process training data and acquire a trainer and
| optimizer. Used as a contextmanager.
+aside-code("Example").
with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
for epoch in trainer.epochs(gold):
for docs, golds in epoch:
state = nlp.update(docs, golds, sgd=optimizer)
+table(["Name", "Type", "Description"])
+row
+cell #[code gold_tuples]
+cell iterable
+cell Gold-standard training data.
+row
+cell #[code **cfg]
+cell -
+cell Config parameters.
+footrow
+cell yields
+cell tuple
+cell A trainer and 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
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.
+footrow
+cell yields
+cell tuple
+cell Tuples of #[code Doc] and #[code GoldParse] objects.
+h(2, "to_disk") Language.to_disk
+tag method
p Save the current state to a directory.
+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 **exclude]
+cell -
+cell Named attributes to prevent from being saved.
+h(2, "from_disk") Language.from_disk
+tag method
p Loads state from a directory. Modifies the object in place and returns it.
+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 **exclude]
+cell -
+cell Named attributes to prevent from being loaded.
+footrow
+cell returns
+cell #[code Language]
+cell The modified #[code Language] object.
+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 **exclude]
+cell -
+cell Named attributes to prevent from being serialized.
+footrow
+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 **exclude]
+cell -
+cell Named attributes to prevent from being loaded.
+footrow
+cell returns
+cell #[code Language]
+cell The #[code Language] object.
+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 make_doc]
+cell #[code lambda text: Doc]
+cell Create a #[code Doc] object from unicode text.
+row
+cell #[code pipeline]
+cell list
+cell Sequence of annotation functions.
+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.
+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].