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51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
from typing import Optional, Callable
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from thinc.api import Model
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from .stop_words import STOP_WORDS
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from .lex_attrs import LEX_ATTRS
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from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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from .punctuation import TOKENIZER_SUFFIXES
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from .syntax_iterators import SYNTAX_ITERATORS
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from ...language import Language, BaseDefaults
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from ...pipeline import Lemmatizer
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class PersianDefaults(BaseDefaults):
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tokenizer_exceptions = TOKENIZER_EXCEPTIONS
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suffixes = TOKENIZER_SUFFIXES
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lex_attr_getters = LEX_ATTRS
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syntax_iterators = SYNTAX_ITERATORS
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stop_words = STOP_WORDS
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writing_system = {"direction": "rtl", "has_case": False, "has_letters": True}
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class Persian(Language):
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lang = "fa"
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Defaults = PersianDefaults
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@Persian.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 Lemmatizer(
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nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
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)
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__all__ = ["Persian"]
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