mirror of
https://github.com/explosion/spaCy.git
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64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
from typing import Callable, Optional
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from thinc.api import Model
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from ...attrs import LANG
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from ...language import BaseDefaults, Language
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from ...lookups import Lookups
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from ...util import update_exc
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from ..tokenizer_exceptions import BASE_EXCEPTIONS
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from .lemmatizer import MacedonianLemmatizer
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from .lex_attrs import LEX_ATTRS
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from .stop_words import STOP_WORDS
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from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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class MacedonianDefaults(BaseDefaults):
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lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
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lex_attr_getters[LANG] = lambda text: "mk"
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# Optional: replace flags with custom functions, e.g. like_num()
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lex_attr_getters.update(LEX_ATTRS)
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# Merge base exceptions and custom tokenizer exceptions
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tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
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stop_words = STOP_WORDS
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@classmethod
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def create_lemmatizer(cls, nlp=None, lookups=None):
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if lookups is None:
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lookups = Lookups()
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return MacedonianLemmatizer(lookups)
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class Macedonian(Language):
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lang = "mk"
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Defaults = MacedonianDefaults
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@Macedonian.factory(
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"lemmatizer",
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assigns=["token.lemma"],
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default_config={
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"model": None,
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"mode": "rule",
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"overwrite": False,
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"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
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},
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default_score_weights={"lemma_acc": 1.0},
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)
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def make_lemmatizer(
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nlp: Language,
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model: Optional[Model],
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name: str,
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mode: str,
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overwrite: bool,
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scorer: Optional[Callable],
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):
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return MacedonianLemmatizer(
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nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
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)
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__all__ = ["Macedonian"]
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