mirror of https://github.com/explosion/spaCy.git
371 lines
14 KiB
Python
371 lines
14 KiB
Python
# 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 shutil
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from .tokenizer import Tokenizer
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from .vocab import Vocab
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from .tagger import Tagger
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from .matcher import Matcher
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from .lemmatizer import Lemmatizer
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from .train import Trainer
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from .syntax.parser import get_templates
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from .syntax.nonproj import PseudoProjectivity
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from .pipeline import DependencyParser, EntityRecognizer
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from .syntax.arc_eager import ArcEager
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from .syntax.ner import BiluoPushDown
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from .compat import json_dumps
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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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class BaseDefaults(object):
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@classmethod
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def create_lemmatizer(cls, nlp=None):
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return Lemmatizer(cls.lemma_index, cls.lemma_exc, cls.lemma_rules)
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@classmethod
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def create_vocab(cls, nlp=None):
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lemmatizer = cls.create_lemmatizer(nlp)
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if nlp is None or nlp.path is None:
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lex_attr_getters = dict(cls.lex_attr_getters)
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# This is very messy, but it's the minimal working fix to Issue #639.
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# This defaults stuff needs to be refactored (again)
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lex_attr_getters[IS_STOP] = lambda string: string.lower() in cls.stop_words
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vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map,
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lemmatizer=lemmatizer)
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else:
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vocab = Vocab.load(nlp.path, lex_attr_getters=cls.lex_attr_getters,
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tag_map=cls.tag_map, lemmatizer=lemmatizer)
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for tag_str, exc in cls.morph_rules.items():
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for orth_str, attrs in exc.items():
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vocab.morphology.add_special_case(tag_str, orth_str, attrs)
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return vocab
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@classmethod
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def add_vectors(cls, nlp=None):
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if nlp is None or nlp.path is None:
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return False
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else:
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vec_path = nlp.path / 'vocab' / 'vec.bin'
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if vec_path.exists():
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return lambda vocab: vocab.load_vectors_from_bin_loc(vec_path)
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@classmethod
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def create_tokenizer(cls, nlp=None):
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rules = cls.tokenizer_exceptions
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if cls.token_match:
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token_match = cls.token_match
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if cls.prefixes:
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prefix_search = util.compile_prefix_regex(cls.prefixes).search
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else:
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prefix_search = None
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if cls.suffixes:
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suffix_search = util.compile_suffix_regex(cls.suffixes).search
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else:
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suffix_search = None
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if cls.infixes:
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infix_finditer = util.compile_infix_regex(cls.infixes).finditer
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else:
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infix_finditer = None
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vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
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return Tokenizer(vocab, rules=rules,
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prefix_search=prefix_search, suffix_search=suffix_search,
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infix_finditer=infix_finditer, token_match=token_match)
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@classmethod
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def create_tagger(cls, nlp=None):
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if nlp is None:
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return Tagger(cls.create_vocab(), features=cls.tagger_features)
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elif nlp.path is False:
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return Tagger(nlp.vocab, features=cls.tagger_features)
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elif nlp.path is None or not (nlp.path / 'pos').exists():
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return None
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else:
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return Tagger.load(nlp.path / 'pos', nlp.vocab)
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@classmethod
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def create_parser(cls, nlp=None, **cfg):
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if nlp is None:
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return DependencyParser(cls.create_vocab(), features=cls.parser_features,
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**cfg)
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elif nlp.path is False:
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return DependencyParser(nlp.vocab, features=cls.parser_features, **cfg)
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elif nlp.path is None or not (nlp.path / 'deps').exists():
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return None
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else:
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return DependencyParser.load(nlp.path / 'deps', nlp.vocab, **cfg)
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@classmethod
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def create_entity(cls, nlp=None, **cfg):
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if nlp is None:
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return EntityRecognizer(cls.create_vocab(), features=cls.entity_features, **cfg)
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elif nlp.path is False:
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return EntityRecognizer(nlp.vocab, features=cls.entity_features, **cfg)
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elif nlp.path is None or not (nlp.path / 'ner').exists():
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return None
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else:
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return EntityRecognizer.load(nlp.path / 'ner', nlp.vocab, **cfg)
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@classmethod
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def create_matcher(cls, nlp=None):
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if nlp is None:
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return Matcher(cls.create_vocab())
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elif nlp.path is False:
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return Matcher(nlp.vocab)
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elif nlp.path is None or not (nlp.path / 'vocab').exists():
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return None
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else:
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return Matcher.load(nlp.path / 'vocab', nlp.vocab)
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@classmethod
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def create_pipeline(self, nlp=None):
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pipeline = []
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if nlp is None:
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return []
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if nlp.tagger:
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pipeline.append(nlp.tagger)
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if nlp.parser:
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pipeline.append(nlp.parser)
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pipeline.append(PseudoProjectivity.deprojectivize)
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if nlp.entity:
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pipeline.append(nlp.entity)
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return pipeline
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token_match = TOKEN_MATCH
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prefixes = tuple(TOKENIZER_PREFIXES)
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suffixes = tuple(TOKENIZER_SUFFIXES)
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infixes = tuple(TOKENIZER_INFIXES)
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tag_map = dict(TAG_MAP)
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tokenizer_exceptions = {}
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parser_features = get_templates('parser')
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entity_features = get_templates('ner')
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tagger_features = Tagger.feature_templates # TODO -- fix this
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stop_words = set()
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lemma_rules = {}
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lemma_exc = {}
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lemma_index = {}
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morph_rules = {}
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lex_attr_getters = LEX_ATTRS
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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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"""
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Defaults = BaseDefaults
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lang = None
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@classmethod
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def setup_directory(cls, path, **configs):
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"""
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Initialise a model directory.
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"""
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for name, config in configs.items():
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directory = path / name
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if directory.exists():
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shutil.rmtree(str(directory))
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directory.mkdir()
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with (directory / 'config.json').open('w') as file_:
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data = json_dumps(config)
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file_.write(data)
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if not (path / 'vocab').exists():
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(path / 'vocab').mkdir()
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@classmethod
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@contextmanager
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def train(cls, path, gold_tuples, **configs):
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parser_cfg = configs.get('deps', {})
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if parser_cfg.get('pseudoprojective'):
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# preprocess training data here before ArcEager.get_labels() is called
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gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
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for subdir in ('deps', 'ner', 'pos'):
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if subdir not in configs:
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configs[subdir] = {}
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if parser_cfg:
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configs['deps']['actions'] = ArcEager.get_actions(gold_parses=gold_tuples)
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if 'ner' in configs:
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configs['ner']['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples)
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cls.setup_directory(path, **configs)
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self = cls(
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path=path,
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vocab=False,
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tokenizer=False,
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tagger=False,
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parser=False,
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entity=False,
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matcher=False,
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vectors=False,
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pipeline=False)
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self.vocab = self.Defaults.create_vocab(self)
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self.tokenizer = self.Defaults.create_tokenizer(self)
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self.tagger = self.Defaults.create_tagger(self)
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self.parser = self.Defaults.create_parser(self)
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self.entity = self.Defaults.create_entity(self)
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self.pipeline = self.Defaults.create_pipeline(self)
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yield Trainer(self, gold_tuples)
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self.end_training()
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self.save_to_directory(path)
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def __init__(self, **overrides):
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"""
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Create or load the pipeline.
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Arguments:
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**overrides: Keyword arguments indicating which defaults to override.
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Returns:
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Language: The newly constructed object.
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"""
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if 'data_dir' in overrides and 'path' not in overrides:
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raise ValueError("The argument 'data_dir' has been renamed to 'path'")
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path = util.ensure_path(overrides.get('path', True))
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if path is True:
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path = util.get_data_path() / self.lang
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if not path.exists() and 'path' not in overrides:
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path = None
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self.meta = overrides.get('meta', {})
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self.path = path
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self.vocab = self.Defaults.create_vocab(self) \
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if 'vocab' not in overrides \
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else overrides['vocab']
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add_vectors = self.Defaults.add_vectors(self) \
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if 'add_vectors' not in overrides \
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else overrides['add_vectors']
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if self.vocab and add_vectors:
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add_vectors(self.vocab)
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self.tokenizer = self.Defaults.create_tokenizer(self) \
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if 'tokenizer' not in overrides \
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else overrides['tokenizer']
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self.tagger = self.Defaults.create_tagger(self) \
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if 'tagger' not in overrides \
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else overrides['tagger']
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self.parser = self.Defaults.create_parser(self) \
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if 'parser' not in overrides \
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else overrides['parser']
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self.entity = self.Defaults.create_entity(self) \
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if 'entity' not in overrides \
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else overrides['entity']
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self.matcher = self.Defaults.create_matcher(self) \
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if 'matcher' not in overrides \
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else overrides['matcher']
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if 'make_doc' in overrides:
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self.make_doc = overrides['make_doc']
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elif 'create_make_doc' in overrides:
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self.make_doc = overrides['create_make_doc'](self)
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elif not hasattr(self, 'make_doc'):
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self.make_doc = lambda text: self.tokenizer(text)
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if 'pipeline' in overrides:
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self.pipeline = overrides['pipeline']
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elif 'create_pipeline' in overrides:
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self.pipeline = overrides['create_pipeline'](self)
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else:
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self.pipeline = [self.tagger, self.parser, self.matcher, self.entity]
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def __call__(self, text, tag=True, parse=True, entity=True):
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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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is preserved.
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Argsuments:
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text (unicode): The text to be processed.
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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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>>> tokens = nlp('An example sentence. Another example sentence.')
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>>> tokens[0].orth_, 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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if self.entity and entity:
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# Add any of the entity labels already set, in case we don't have them.
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for token in doc:
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if token.ent_type != 0:
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self.entity.add_label(token.ent_type)
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skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
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for proc in self.pipeline:
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if proc and not skip.get(proc):
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proc(doc)
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return doc
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def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2, batch_size=1000):
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"""
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Process texts as a stream, and yield Doc objects in order.
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Supports GIL-free multi-threading.
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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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"""
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skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
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stream = (self.make_doc(text) for text in texts)
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for proc in self.pipeline:
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if proc and not skip.get(proc):
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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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stream = (proc(item) for item in stream)
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for doc in stream:
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yield doc
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def save_to_directory(self, path):
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"""
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Save the Vocab, StringStore and pipeline to a directory.
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Arguments:
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path (string or pathlib path): Path to save the model.
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"""
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configs = {
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'pos': self.tagger.cfg if self.tagger else {},
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'deps': self.parser.cfg if self.parser else {},
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'ner': self.entity.cfg if self.entity else {},
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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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path.mkdir()
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self.setup_directory(path, **configs)
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strings_loc = path / 'vocab' / 'strings.json'
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with strings_loc.open('w', encoding='utf8') as file_:
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self.vocab.strings.dump(file_)
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self.vocab.dump(path / 'vocab' / 'lexemes.bin')
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# TODO: Word vectors?
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if self.tagger:
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self.tagger.model.dump(str(path / 'pos' / 'model'))
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if self.parser:
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self.parser.model.dump(str(path / 'deps' / 'model'))
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if self.entity:
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self.entity.model.dump(str(path / 'ner' / 'model'))
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def end_training(self, path=None):
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if self.tagger:
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self.tagger.model.end_training()
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if self.parser:
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self.parser.model.end_training()
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if self.entity:
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self.entity.model.end_training()
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# NB: This is slightly different from before --- we no longer default
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# to taking nlp.path
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if path is not None:
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self.save_to_directory(path)
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