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87c329c711
For languages without provided models and with lemmatizer rules in `spacy-lookups-data`, make the rule-based lemmatizer the default: Bengali, Persian, Norwegian, Swedish
43 lines
1.1 KiB
Python
43 lines
1.1 KiB
Python
from typing import Optional
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from thinc.api import Model
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from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
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from .stop_words import STOP_WORDS
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from ...language import Language
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from ...lookups import Lookups
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from ...pipeline import Lemmatizer
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class BengaliDefaults(Language.Defaults):
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tokenizer_exceptions = TOKENIZER_EXCEPTIONS
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prefixes = TOKENIZER_PREFIXES
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suffixes = TOKENIZER_SUFFIXES
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infixes = TOKENIZER_INFIXES
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stop_words = STOP_WORDS
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class Bengali(Language):
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lang = "bn"
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Defaults = BengaliDefaults
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@Bengali.factory(
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"lemmatizer",
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assigns=["token.lemma"],
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default_config={"model": None, "mode": "rule", "lookups": None},
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scores=["lemma_acc"],
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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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lookups: Optional[Lookups],
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):
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lookups = Lemmatizer.load_lookups(nlp.lang, mode, lookups)
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return Lemmatizer(nlp.vocab, model, name, mode=mode, lookups=lookups)
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__all__ = ["Bengali"]
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