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345 lines
8.8 KiB
Plaintext
345 lines
8.8 KiB
Plaintext
//- 💫 DOCS > API > LANGUAGE
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include ../../_includes/_mixins
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p
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| A text-processing pipeline. Usually you'll load this once per process,
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| and pass the instance around your application.
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+h(2, "init") Language.__init__
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+tag method
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p Initialise a #[code Language] object.
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+aside-code("Example").
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from spacy.language import Language
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nlp = Language(pipeline=['token_vectors', 'tags',
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'dependencies'])
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from spacy.lang.en import English
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nlp = English()
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell
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| A #[code Vocab] object. If #[code True], a vocab is created via
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| #[code Language.Defaults.create_vocab].
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+row
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+cell #[code make_doc]
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+cell callable
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+cell
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| A function that takes text and returns a #[code Doc] object.
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| Usually a #[code Tokenizer].
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+row
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+cell #[code pipeline]
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+cell list
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+cell
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| A list of annotation processes or IDs of annotation, processes,
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| e.g. a #[code Tagger] object, or #[code 'tagger']. IDs are looked
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| up in #[code Language.Defaults.factories].
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+row
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+cell #[code meta]
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+cell dict
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+cell
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| Custom meta data for the #[code Language] class. Is written to by
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| models to add model meta data.
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+footrow
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+cell returns
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+cell #[code Language]
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+cell The newly constructed object.
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+h(2, "call") Language.__call__
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+tag method
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p
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| Apply the pipeline to some text. The text can span multiple sentences,
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| and can contain arbtrary whitespace. Alignment into the original string
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| is preserved.
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+aside-code("Example").
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doc = nlp(u'An example sentence. Another sentence.')
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assert (doc[0].text, doc[0].head.tag_) == ('An', 'NN')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code text]
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+cell unicode
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+cell The text to be processed.
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+row
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+cell #[code **disabled]
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+cell -
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+cell Elements of the pipeline that should not be run.
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+footrow
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+cell returns
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+cell #[code Doc]
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+cell A container for accessing the annotations.
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+h(2, "update") Language.update
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+tag method
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p Update the models in the pipeline.
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+aside-code("Example").
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with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
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for epoch in trainer.epochs(gold):
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for docs, golds in epoch:
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state = nlp.update(docs, golds, sgd=optimizer)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code docs]
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+cell iterable
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+cell A batch of #[code Doc] objects.
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+row
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+cell #[code golds]
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+cell iterable
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+cell A batch of #[code GoldParse] objects.
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+row
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+cell #[code drop]
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+cell float
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+cell The dropout rate.
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+row
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+cell #[code sgd]
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+cell callable
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+cell An optimizer.
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+footrow
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+cell returns
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+cell dict
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+cell Results from the update.
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+h(2, "begin_training") Language.begin_training
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+tag contextmanager
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p
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| Allocate models, pre-process training data and acquire a trainer and
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| optimizer. Used as a contextmanager.
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+aside-code("Example").
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with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
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for epoch in trainer.epochs(gold):
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for docs, golds in epoch:
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state = nlp.update(docs, golds, sgd=optimizer)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code gold_tuples]
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+cell iterable
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+cell Gold-standard training data.
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+row
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+cell #[code **cfg]
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+cell -
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+cell Config parameters.
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+footrow
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+cell yields
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+cell tuple
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+cell A trainer and an optimizer.
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+h(2, "use_params") Language.use_params
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+tag contextmanager
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+tag method
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p
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| Replace weights of models in the pipeline with those provided in the
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| params dictionary. Can be used as a contextmanager, in which case, models
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| go back to their original weights after the block.
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+aside-code("Example").
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with nlp.use_params(optimizer.averages):
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nlp.to_disk('/tmp/checkpoint')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code params]
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+cell dict
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+cell A dictionary of parameters keyed by model ID.
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+row
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+cell #[code **cfg]
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+cell -
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+cell Config parameters.
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+h(2, "pipe") Language.pipe
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+tag method
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p
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| Process texts as a stream, and yield #[code Doc] objects in order.
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| Supports GIL-free multi-threading.
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+aside-code("Example").
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texts = [u'One document.', u'...', u'Lots of documents']
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for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
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assert doc.is_parsed
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code texts]
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+cell -
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+cell A sequence of unicode objects.
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+row
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+cell #[code n_threads]
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+cell int
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+cell
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| The number of worker threads to use. If #[code -1], OpenMP will
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| decide how many to use at run time. Default is #[code 2].
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+row
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+cell #[code batch_size]
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+cell int
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+cell The number of texts to buffer.
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+footrow
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+cell yields
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+cell #[code Doc]
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+cell Documents in the order of the original text.
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+h(2, "to_disk") Language.to_disk
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+tag method
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p Save the current state to a directory.
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+aside-code("Example").
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nlp.to_disk('/path/to/models')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code path]
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+cell unicode or #[code Path]
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+cell
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| A path to a directory, which will be created if it doesn't exist.
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| Paths may be either strings or #[code Path]-like objects.
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+row
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+cell #[code **exclude]
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+cell -
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+cell Named attributes to prevent from being saved.
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+h(2, "from_disk") Language.from_disk
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+tag method
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p Loads state from a directory. Modifies the object in place and returns it.
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+aside-code("Example").
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from spacy.language import Language
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nlp = Language().from_disk('/path/to/models')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code path]
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+cell unicode or #[code Path]
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+cell
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| A path to a directory. Paths may be either strings or
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| #[code Path]-like objects.
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+row
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+cell #[code **exclude]
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+cell -
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+cell Named attributes to prevent from being loaded.
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+footrow
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+cell returns
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+cell #[code Language]
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+cell The modified #[code Language] object.
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+h(2, "to_bytes") Language.to_bytes
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+tag method
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p Serialize the current state to a binary string.
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+aside-code("Example").
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nlp_bytes = nlp.to_bytes()
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code **exclude]
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+cell -
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+cell Named attributes to prevent from being serialized.
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+footrow
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+cell returns
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+cell bytes
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+cell The serialized form of the #[code Language] object.
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+h(2, "from_bytes") Language.from_bytes
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+tag method
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p Load state from a binary string.
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+aside-code("Example").
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fron spacy.lang.en import English
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nlp_bytes = nlp.to_bytes()
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nlp2 = English()
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nlp2.from_bytes(nlp_bytes)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code bytes_data]
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+cell bytes
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+cell The data to load from.
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+row
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+cell #[code **exclude]
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+cell -
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+cell Named attributes to prevent from being loaded.
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+footrow
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+cell returns
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+cell #[code Language]
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+cell The #[code Language] object.
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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell A container for the lexical types.
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+row
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+cell #[code make_doc]
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+cell #[code lambda text: Doc]
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+cell Create a #[code Doc] object from unicode text.
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+row
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+cell #[code pipeline]
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+cell list
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+cell Sequence of annotation functions.
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+row
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+cell #[code meta]
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+cell dict
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+cell
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| Custom meta data for the Language class. If a model is loaded,
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| contains meta data of the model.
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+h(2, "class-attributes") Class attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code Defaults]
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+cell class
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+cell
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| Settings, data and factory methods for creating the
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| #[code nlp] object and processing pipeline.
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+row
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+cell #[code lang]
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+cell unicode
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+cell
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| Two-letter language ID, i.e.
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| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code].
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