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This commit is contained in:
Matthew Honnibal 2025-05-22 00:18:44 +02:00
parent 752c2dd403
commit 1da76e9aca
2 changed files with 66 additions and 61 deletions
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1
spacy/language.py
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@ -184,6 +184,7 @@ class Language:
DOCS: https://spacy.io/api/language#init
"""
from .pipeline.factories import register_factories
register_factories()
# We're only calling this to import all factories provided via entry
# points. The factory decorator applied to these functions takes care

126
spacy/registrations.py
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@ -21,13 +21,38 @@ def populate_registry() -> None:
return
# Import all necessary modules
from .lang.ja import create_tokenizer as create_japanese_tokenizer
from .lang.ko import create_tokenizer as create_korean_tokenizer
from .lang.th import create_thai_tokenizer
from .lang.vi import create_vietnamese_tokenizer
from .lang.zh import create_chinese_tokenizer
from .language import load_lookups_data
from .matcher.levenshtein import make_levenshtein_compare
from .ml.models.entity_linker import (
create_candidates,
create_candidates_batch,
empty_kb,
empty_kb_for_config,
load_kb,
)
from .pipeline.attributeruler import make_attribute_ruler_scorer
from .pipeline.dep_parser import make_parser_scorer
# Import the functions we refactored by removing direct registry decorators
from .pipeline.entity_linker import make_entity_linker_scorer
from .pipeline.entityruler import (
make_entity_ruler_scorer as make_entityruler_scorer,
)
from .pipeline.lemmatizer import make_lemmatizer_scorer
from .pipeline.morphologizer import make_morphologizer_scorer
from .pipeline.ner import make_ner_scorer
from .pipeline.senter import make_senter_scorer
from .pipeline.span_finder import make_span_finder_scorer
from .pipeline.span_ruler import (
make_overlapping_labeled_spans_scorer,
make_preserve_existing_ents_filter,
make_prioritize_new_ents_filter,
)
from .pipeline.spancat import (
build_ngram_range_suggester,
build_ngram_suggester,
@ -35,32 +60,11 @@ def populate_registry() -> None:
make_spancat_scorer,
)
# Import the functions we refactored by removing direct registry decorators
from .pipeline.entity_linker import make_entity_linker_scorer
from .pipeline.span_ruler import (
make_overlapping_labeled_spans_scorer,
make_prioritize_new_ents_filter,
make_preserve_existing_ents_filter,
)
from .pipeline.attributeruler import make_attribute_ruler_scorer
from .pipeline.dep_parser import make_parser_scorer
from .pipeline.morphologizer import make_morphologizer_scorer
from .ml.models.entity_linker import load_kb, empty_kb_for_config, empty_kb
from .ml.models.entity_linker import create_candidates
from .ml.models.entity_linker import create_candidates_batch
from .language import load_lookups_data
from .lang.ja import create_tokenizer as create_japanese_tokenizer
from .lang.zh import create_chinese_tokenizer
from .lang.ko import create_tokenizer as create_korean_tokenizer
from .lang.vi import create_vietnamese_tokenizer
from .lang.th import create_thai_tokenizer
# Import all pipeline components that were using registry decorators
from .pipeline.tagger import make_tagger_scorer
from .pipeline.textcat import make_textcat_scorer
from .pipeline.textcat_multilabel import make_textcat_multilabel_scorer
from .util import make_first_longest_spans_filter, registry
from .matcher.levenshtein import make_levenshtein_compare
# Register miscellaneous components
registry.misc("spacy.first_longest_spans_filter.v1")(
@ -88,6 +92,39 @@ def populate_registry() -> None:
# For the registry that was previously decorated
# Import ML components that use registry
from .language import create_tokenizer
from .ml._precomputable_affine import PrecomputableAffine
from .ml.callbacks import (
create_models_and_pipes_with_nvtx_range,
create_models_with_nvtx_range,
)
from .ml.extract_ngrams import extract_ngrams
from .ml.extract_spans import extract_spans
# Import decorator-removed ML components
from .ml.featureextractor import FeatureExtractor
from .ml.models.entity_linker import build_nel_encoder
from .ml.models.multi_task import (
create_pretrain_characters,
create_pretrain_vectors,
)
from .ml.models.parser import build_tb_parser_model
from .ml.models.span_finder import build_finder_model
from .ml.models.spancat import (
build_linear_logistic,
build_mean_max_reducer,
build_spancat_model,
)
from .ml.models.tagger import build_tagger_model
from .ml.models.textcat import (
build_bow_text_classifier,
build_bow_text_classifier_v3,
build_reduce_text_classifier,
build_simple_cnn_text_classifier,
build_text_classifier_lowdata,
build_text_classifier_v2,
build_textcat_parametric_attention_v1,
)
from .ml.models.tok2vec import (
BiLSTMEncoder,
CharacterEmbed,
@ -98,53 +135,20 @@ def populate_registry() -> None:
build_Tok2Vec_model,
tok2vec_listener_v1,
)
# Import decorator-removed ML components
from .ml.featureextractor import FeatureExtractor
from .ml.extract_spans import extract_spans
from .ml.extract_ngrams import extract_ngrams
from .ml.models.entity_linker import build_nel_encoder
from .ml.models.textcat import (
build_simple_cnn_text_classifier,
build_bow_text_classifier,
build_bow_text_classifier_v3,
build_text_classifier_v2,
build_text_classifier_lowdata,
build_textcat_parametric_attention_v1,
build_reduce_text_classifier,
)
from .ml.models.spancat import (
build_linear_logistic,
build_mean_max_reducer,
build_spancat_model,
)
from .ml.models.span_finder import build_finder_model
from .ml.models.parser import build_tb_parser_model
from .ml.models.multi_task import (
create_pretrain_vectors,
create_pretrain_characters,
)
from .ml.models.tagger import build_tagger_model
from .ml.staticvectors import StaticVectors
from .ml._precomputable_affine import PrecomputableAffine
from .ml.tb_framework import TransitionModel
from .language import create_tokenizer
from .training.callbacks import create_copy_from_base_model
from .ml.callbacks import (
create_models_with_nvtx_range,
create_models_and_pipes_with_nvtx_range,
)
from .training.loggers import console_logger, console_logger_v3
from .training.batchers import (
configure_minibatch_by_padded_size,
configure_minibatch_by_words,
configure_minibatch,
)
from .training.augment import (
create_combined_augmenter,
create_lower_casing_augmenter,
create_orth_variants_augmenter,
)
from .training.batchers import (
configure_minibatch,
configure_minibatch_by_padded_size,
configure_minibatch_by_words,
)
from .training.callbacks import create_copy_from_base_model
from .training.loggers import console_logger, console_logger_v3
# Register scorers
registry.scorers("spacy.tagger_scorer.v1")(make_tagger_scorer)