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