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https://github.com/explosion/spaCy.git
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f99d6d5e39
* Add scorer option to components Add an optional `scorer` parameter to all pipeline components. If a scoring function is provided, it overrides the default scoring method for that component. * Add registered scorers for all components * Add `scorers` registry * Move all scoring methods outside of components as independent functions and register * Use the registered scoring methods as defaults in configs and inits Additional: * The scoring methods no longer have access to the full component, so use settings from `cfg` as default scorer options to handle settings such as `labels`, `threshold`, and `positive_label` * The `attribute_ruler` scoring method no longer has access to the patterns, so all scoring methods are called * Bug fix: `spancat` scoring method is updated to set `allow_overlap` to score overlapping spans correctly * Update Russian lemmatizer to use direct score method * Check type of cfg in Pipe.score * Fix check * Update spacy/pipeline/sentencizer.pyx Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Remove validate_examples from scoring functions * Use Pipe.labels instead of Pipe.cfg["labels"] Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
48 lines
1.1 KiB
Python
48 lines
1.1 KiB
Python
from typing import Optional, Callable
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from thinc.api import Model
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from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
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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 .lemmatizer import UkrainianLemmatizer
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from ...language import Language
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class UkrainianDefaults(Language.Defaults):
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tokenizer_exceptions = TOKENIZER_EXCEPTIONS
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lex_attr_getters = LEX_ATTRS
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stop_words = STOP_WORDS
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class Ukrainian(Language):
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lang = "uk"
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Defaults = UkrainianDefaults
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@Ukrainian.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": "pymorphy2",
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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 UkrainianLemmatizer(
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nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
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)
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__all__ = ["Ukrainian"]
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