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Update docstrings and API docs for Language class
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@ -115,14 +115,26 @@ class BaseDefaults(object):
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class Language(object):
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"""
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A text-processing pipeline. Usually you'll load this once per process, and
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pass the instance around your program.
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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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"""
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Defaults = BaseDefaults
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lang = None
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def __init__(self, vocab=True, make_doc=True, pipeline=None, meta={}):
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"""Initialise a Language object.
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vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via
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`Language.Defaults.create_vocab`.
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make_doc (function): A function that takes text and returns a `Doc`
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object. Usually a `Tokenizer`.
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pipeline (list): A list of annotation processes or IDs of annotation,
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processes, e.g. a `Tagger` object, or `'tagger'`. IDs are looked
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up in `Language.Defaults.factories`.
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meta (dict): Custom meta data for the Language class. Is written to by
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models to add model meta data.
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RETURNS (Language): The newly constructed object.
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"""
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self.meta = dict(meta)
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if vocab is True:
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@ -146,23 +158,17 @@ class Language(object):
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self.pipeline = []
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def __call__(self, text, state=None, **disabled):
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"""
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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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"""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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Args:
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text (unicode): The text to be processed.
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state: Arbitrary
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text (unicode): The text to be processed.
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**disabled: Elements of the pipeline that should not be run.
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RETURNS (Doc): A container for accessing the annotations.
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Returns:
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doc (Doc): A container for accessing the annotations.
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Example:
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>>> from spacy.en import English
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>>> nlp = English()
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EXAMPLE:
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>>> tokens = nlp('An example sentence. Another example sentence.')
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>>> tokens[0].orth_, tokens[0].head.tag_
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>>> tokens[0].text, tokens[0].head.tag_
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('An', 'NN')
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"""
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doc = self.make_doc(text)
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@ -174,16 +180,28 @@ class Language(object):
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return doc
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def update(self, docs, golds, state=None, drop=0., sgd=None):
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"""Update the models in the pipeline.
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docs (iterable): A batch of `Doc` objects.
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golds (iterable): A batch of `GoldParse` objects.
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drop (float): The droput rate.
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sgd (function): An optimizer.
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RETURNS (dict): Results from the update.
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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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"""
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grads = {}
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def get_grads(W, dW, key=None):
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grads[key] = (W, dW)
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state = {} if state is None else state
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for process in self.pipeline:
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if hasattr(process, 'update'):
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state = process.update(docs, golds,
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state=state,
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drop=drop,
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sgd=get_grads)
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state = process.update(docs, golds, state=state, drop=drop,
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sgd=get_grads)
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else:
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process(docs, state=state)
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if sgd is not None:
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@ -198,6 +216,19 @@ class Language(object):
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@contextmanager
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def begin_training(self, gold_tuples, **cfg):
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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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gold_tuples (iterable): Gold-standard training data.
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**cfg: Config parameters.
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YIELDS (tuple): A trainer and an optimizer.
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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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"""
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# Populate vocab
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for _, annots_brackets in gold_tuples:
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for annots, _ in annots_brackets:
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@ -220,6 +251,17 @@ class Language(object):
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@contextmanager
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def use_params(self, params, **cfg):
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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,
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models go back to their original weights after the block.
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params (dict): A dictionary of parameters keyed by model ID.
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**cfg: Config parameters.
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EXAMPLE:
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>>> with nlp.use_params(optimizer.averages):
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>>> nlp.to_disk('/tmp/checkpoint')
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"""
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contexts = [pipe.use_params(params) for pipe
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in self.pipeline if hasattr(pipe, 'use_params')]
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# TODO: Having trouble with contextlib
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@ -237,16 +279,20 @@ class Language(object):
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pass
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def pipe(self, texts, n_threads=2, batch_size=1000, **disabled):
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"""
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Process texts as a stream, and yield Doc objects in order.
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"""Process texts as a stream, and yield `Doc` objects in order. Supports
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GIL-free multi-threading.
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Supports GIL-free multi-threading.
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texts (iterator): A sequence of texts to process.
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n_threads (int): The number of worker threads to use. If -1, OpenMP will
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decide how many to use at run time. Default is 2.
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batch_size (int): The number of texts to buffer.
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**disabled: Pipeline components to exclude.
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YIELDS (Doc): Documents in the order of the original text.
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Arguments:
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texts (iterator)
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tag (bool)
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parse (bool)
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entity (bool)
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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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"""
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#stream = ((self.make_doc(text), None) for text in texts)
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stream = ((doc, {}) for doc in texts)
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@ -254,7 +300,6 @@ class Language(object):
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name = getattr(proc, 'name', None)
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if name in disabled and not disabled[name]:
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continue
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if hasattr(proc, 'pipe'):
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stream = proc.pipe(stream, n_threads=n_threads, batch_size=batch_size)
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else:
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@ -265,11 +310,12 @@ class Language(object):
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def to_disk(self, path, **exclude):
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"""Save the current state to a directory.
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Args:
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path: A path to a directory, which will be created if it doesn't
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exist. Paths may be either strings or pathlib.Path-like
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objects.
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**exclude: Prevent named attributes from being saved.
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path (unicode or Path): A path to a directory, which will be created if
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it doesn't exist. Paths may be either strings or `Path`-like objects.
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**exclude: Named attributes to prevent from being saved.
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EXAMPLE:
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>>> nlp.to_disk('/path/to/models')
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"""
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path = util.ensure_path(path)
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if not path.exists():
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@ -288,12 +334,17 @@ class Language(object):
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dill.dump(props, file_)
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def from_disk(self, path, **exclude):
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"""Load the current state from a directory.
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"""Loads state from a directory. Modifies the object in place and
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returns it.
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Args:
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path: A path to a directory. Paths may be either strings or
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pathlib.Path-like objects.
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**exclude: Prevent named attributes from being saved.
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path (unicode or Path): A path to a directory. Paths may be either
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strings or `Path`-like objects.
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**exclude: Named attributes to prevent from being loaded.
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RETURNS (Language): The modified `Language` object.
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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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"""
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path = util.ensure_path(path)
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for name in path.iterdir():
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@ -307,10 +358,8 @@ class Language(object):
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def to_bytes(self, **exclude):
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"""Serialize the current state to a binary string.
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Args:
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path: A path to a directory. Paths may be either strings or
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pathlib.Path-like objects.
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**exclude: Prevent named attributes from being serialized.
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**exclude: Named attributes to prevent from being serialized.
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RETURNS (bytes): The serialized form of the `Language` object.
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"""
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props = dict(self.__dict__)
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for key in exclude:
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@ -321,9 +370,9 @@ class Language(object):
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def from_bytes(self, bytes_data, **exclude):
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"""Load state from a binary string.
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Args:
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bytes_data (bytes): The data to load from.
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**exclude: Prevent named attributes from being loaded.
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bytes_data (bytes): The data to load from.
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**exclude: Named attributes to prevent from being loaded.
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RETURNS (Language): The `Language` object.
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"""
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props = dill.loads(bytes_data)
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for key, value in props.items():
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@ -2,7 +2,305 @@
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include ../../_includes/_mixins
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p A text processing pipeline.
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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 function
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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 return
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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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tokens = nlp('An example sentence. Another example sentence.')
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tokens[0].text, tokens[0].head.tag_
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# ('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 return
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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 function
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+cell An optimizer.
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+footrow
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+cell return
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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 yield
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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 yield
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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 return
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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.
|
||||
|
||||
+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 return
|
||||
+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 return
|
||||
+cell bytes
|
||||
+cell The serialized form of the #[code Language] object.
|
||||
|
||||
+h(2, "attributes") Attributes
|
||||
|
||||
|
@ -46,109 +344,3 @@ p A text processing pipeline.
|
|||
+cell #[code pipeline]
|
||||
+cell -
|
||||
+cell Sequence of annotation functions.
|
||||
|
||||
|
||||
+h(2, "init") Language.__init__
|
||||
+tag method
|
||||
|
||||
p Create or load the pipeline.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code **overrides]
|
||||
+cell -
|
||||
+cell Keyword arguments indicating which defaults to override.
|
||||
|
||||
+footrow
|
||||
+cell return
|
||||
+cell #[code Language]
|
||||
+cell The newly constructed object.
|
||||
|
||||
+h(2, "call") Language.__call__
|
||||
+tag method
|
||||
|
||||
p Apply the pipeline to a single text.
|
||||
|
||||
+aside-code("Example").
|
||||
from spacy.en import English
|
||||
nlp = English()
|
||||
doc = nlp('An example sentence. Another example sentence.')
|
||||
doc[0].orth_, 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 tag]
|
||||
+cell bool
|
||||
+cell Whether to apply the part-of-speech tagger.
|
||||
|
||||
+row
|
||||
+cell #[code parse]
|
||||
+cell bool
|
||||
+cell Whether to apply the syntactic dependency parser.
|
||||
|
||||
+row
|
||||
+cell #[code entity]
|
||||
+cell bool
|
||||
+cell Whether to apply the named entity recognizer.
|
||||
|
||||
+footrow
|
||||
+cell return
|
||||
+cell #[code Doc]
|
||||
+cell A container for accessing the linguistic 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 yield
|
||||
+cell #[code Doc]
|
||||
+cell Containers for accessing the linguistic annotations.
|
||||
|
||||
+h(2, "save_to_directory") Language.save_to_directory
|
||||
+tag method
|
||||
|
||||
p Save the #[code Vocab], #[code StringStore] and pipeline to a directory.
|
||||
|
||||
+table(["Name", "Type", "Description"])
|
||||
+row
|
||||
+cell #[code path]
|
||||
+cell string or pathlib path
|
||||
+cell Path to save the model.
|
||||
|
||||
+footrow
|
||||
+cell return
|
||||
+cell #[code None]
|
||||
+cell -
|
||||
|
|
Loading…
Reference in New Issue
Block a user