spaCy/spacy/lang/mk/__init__.py
Matthew Honnibal e2dc9b79e1 Format
2024-09-14 11:29:40 +02:00

58 lines
1.5 KiB
Python

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