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Fix formatting, tidy up and remove unused imports
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@ -1,12 +1,9 @@
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# coding: utf8
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from __future__ import absolute_import, unicode_literals
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from contextlib import contextmanager
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import dill
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import numpy
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from thinc.neural import Model
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from thinc.neural.ops import NumpyOps, CupyOps
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from thinc.neural.optimizers import Adam, SGD
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from thinc.neural.optimizers import Adam
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import random
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import ujson
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from collections import OrderedDict
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@ -17,24 +14,20 @@ from .vocab import Vocab
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from .tagger import Tagger
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from .lemmatizer import Lemmatizer
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from .syntax.parser import get_templates
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from .syntax import nonproj
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from .pipeline import NeuralDependencyParser, EntityRecognizer
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from .pipeline import TokenVectorEncoder, NeuralTagger, NeuralEntityRecognizer
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from .pipeline import NeuralLabeller
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from .pipeline import SimilarityHook
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from .pipeline import TextCategorizer
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from . import about
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from .pipeline import NeuralDependencyParser, TokenVectorEncoder, NeuralTagger
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from .pipeline import NeuralEntityRecognizer, SimilarityHook, TextCategorizer
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from .compat import json_dumps, izip
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from .scorer import Scorer
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from ._ml import link_vectors_to_models
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from .attrs import IS_STOP
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from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
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from .lang.tokenizer_exceptions import TOKEN_MATCH
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from .lang.tag_map import TAG_MAP
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from .lang.lex_attrs import LEX_ATTRS
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from . import util
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from .scorer import Scorer
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from ._ml import link_vectors_to_models
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from . import about
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class BaseDefaults(object):
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@ -289,7 +282,7 @@ class Language(object):
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return self.pipeline.pop(self.pipe_names.index(name))
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def __call__(self, text, disable=[]):
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"""'Apply the pipeline to some text. The text can span multiple sentences,
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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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@ -387,7 +380,7 @@ class Language(object):
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get_gold_tuples (function): Function returning gold data
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**cfg: Config parameters.
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returns: An optimizer
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RETURNS: An optimizer
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"""
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# Populate vocab
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if get_gold_tuples is not None:
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