mirror of
https://github.com/explosion/spaCy.git
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Register factories in spacy.registrations, to avoid import-time side-effects
This commit is contained in:
Matthew Honnibal
parent
15bd029be5
commit
c62b9dac0b
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@ -22,13 +22,6 @@ TagMapType = Dict[str, Dict[Union[int, str], Union[int, str]]]
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MorphRulesType = Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
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MorphRulesType = Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
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@Language.factory(
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"attribute_ruler",
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default_config={
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"validate": False,
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"scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
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},
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)
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def make_attribute_ruler(
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def make_attribute_ruler(
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nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
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nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
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):
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):
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@ -39,26 +39,6 @@ subword_features = true
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DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"parser",
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assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_PARSER_MODEL,
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"scorer": {"@scorers": "spacy.parser_scorer.v1"},
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},
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default_score_weights={
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"dep_uas": 0.5,
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"dep_las": 0.5,
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"dep_las_per_type": None,
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"sents_p": None,
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"sents_r": None,
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"sents_f": 0.0,
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},
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)
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def make_parser(
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def make_parser(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -125,29 +105,6 @@ def make_parser(
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)
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)
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@Language.factory(
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"beam_parser",
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assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
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default_config={
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"beam_width": 8,
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"beam_density": 0.01,
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"beam_update_prob": 0.5,
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_PARSER_MODEL,
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"scorer": {"@scorers": "spacy.parser_scorer.v1"},
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},
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default_score_weights={
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"dep_uas": 0.5,
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"dep_las": 0.5,
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"dep_las_per_type": None,
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"sents_p": None,
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"sents_r": None,
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"sents_f": 0.0,
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},
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)
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def make_beam_parser(
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def make_beam_parser(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -39,20 +39,6 @@ subword_features = true
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DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"trainable_lemmatizer",
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assigns=["token.lemma"],
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requires=[],
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default_config={
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"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
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"backoff": "orth",
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"min_tree_freq": 3,
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"overwrite": False,
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"top_k": 1,
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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_edit_tree_lemmatizer(
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def make_edit_tree_lemmatizer(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -40,32 +40,6 @@ subword_features = true
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DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"entity_linker",
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requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
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assigns=["token.ent_kb_id"],
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default_config={
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"model": DEFAULT_NEL_MODEL,
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"labels_discard": [],
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"n_sents": 0,
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"incl_prior": True,
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"incl_context": True,
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"entity_vector_length": 64,
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"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
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"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
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"generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
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"overwrite": True,
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"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
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"use_gold_ents": True,
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"candidates_batch_size": 1,
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"threshold": None,
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},
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default_score_weights={
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"nel_micro_f": 1.0,
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"nel_micro_r": None,
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"nel_micro_p": None,
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},
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)
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def make_entity_linker(
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def make_entity_linker(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -19,24 +19,6 @@ DEFAULT_ENT_ID_SEP = "||"
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PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
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PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
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@Language.factory(
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"entity_ruler",
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assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
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default_config={
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"phrase_matcher_attr": None,
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"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
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"validate": False,
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"overwrite_ents": False,
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"ent_id_sep": DEFAULT_ENT_ID_SEP,
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"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
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},
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default_score_weights={
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"ents_f": 1.0,
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"ents_p": 0.0,
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"ents_r": 0.0,
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"ents_per_type": None,
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},
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)
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def make_entity_ruler(
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def make_entity_ruler(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -73,11 +73,6 @@ def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
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return doc
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return doc
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@Language.factory(
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"token_splitter",
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default_config={"min_length": 25, "split_length": 10},
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retokenizes=True,
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)
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def make_token_splitter(
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def make_token_splitter(
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nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
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nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
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):
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):
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@ -141,10 +136,6 @@ class TokenSplitter:
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util.from_disk(path, serializers, [])
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util.from_disk(path, serializers, [])
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@Language.factory(
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"doc_cleaner",
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default_config={"attrs": {"tensor": None, "_.trf_data": None}, "silent": True},
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)
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def make_doc_cleaner(nlp: Language, name: str, *, attrs: Dict[str, Any], silent: bool):
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def make_doc_cleaner(nlp: Language, name: str, *, attrs: Dict[str, Any], silent: bool):
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return DocCleaner(attrs, silent=silent)
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return DocCleaner(attrs, silent=silent)
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@ -16,17 +16,6 @@ from ..vocab import Vocab
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from .pipe import Pipe
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from .pipe import Pipe
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@Language.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": "lookup",
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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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def make_lemmatizer(
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nlp: Language,
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nlp: Language,
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model: Optional[Model],
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model: Optional[Model],
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@ -47,13 +47,6 @@ maxout_pieces = 3
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DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"morphologizer",
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assigns=["token.morph", "token.pos"],
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default_config={"model": DEFAULT_MORPH_MODEL, "overwrite": True, "extend": False,
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"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"}, "label_smoothing": 0.0},
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default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
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)
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def make_morphologizer(
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def make_morphologizer(
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nlp: Language,
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nlp: Language,
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model: Model,
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model: Model,
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@ -30,10 +30,6 @@ subword_features = true
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DEFAULT_MT_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_MT_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"nn_labeller",
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default_config={"labels": None, "target": "dep_tag_offset", "model": DEFAULT_MT_MODEL}
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)
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def make_nn_labeller(nlp: Language, name: str, model: Model, labels: Optional[dict], target: str):
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def make_nn_labeller(nlp: Language, name: str, model: Model, labels: Optional[dict], target: str):
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return MultitaskObjective(nlp.vocab, model, name)
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return MultitaskObjective(nlp.vocab, model, name)
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@ -36,19 +36,6 @@ subword_features = true
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DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"ner",
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assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"model": DEFAULT_NER_MODEL,
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"incorrect_spans_key": None,
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"scorer": {"@scorers": "spacy.ner_scorer.v1"},
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},
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default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
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)
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def make_ner(
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def make_ner(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -101,21 +88,6 @@ def make_ner(
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)
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)
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@Language.factory(
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"beam_ner",
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assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"model": DEFAULT_NER_MODEL,
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"beam_density": 0.01,
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"beam_update_prob": 0.5,
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"beam_width": 32,
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"incorrect_spans_key": None,
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"scorer": None,
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},
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default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
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)
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def make_beam_ner(
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def make_beam_ner(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -14,12 +14,6 @@ from .senter import senter_score
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BACKWARD_OVERWRITE = False
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BACKWARD_OVERWRITE = False
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@Language.factory(
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"sentencizer",
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assigns=["token.is_sent_start", "doc.sents"],
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default_config={"punct_chars": None, "overwrite": False, "scorer": {"@scorers": "spacy.senter_scorer.v1"}},
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default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
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)
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def make_sentencizer(
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def make_sentencizer(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -34,12 +34,6 @@ subword_features = true
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DEFAULT_SENTER_MODEL = Config().from_str(default_model_config)["model"]
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DEFAULT_SENTER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"senter",
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assigns=["token.is_sent_start"],
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default_config={"model": DEFAULT_SENTER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.senter_scorer.v1"}},
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default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
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)
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def make_senter(nlp: Language, name: str, model: Model, overwrite: bool, scorer: Optional[Callable]):
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def make_senter(nlp: Language, name: str, model: Model, overwrite: bool, scorer: Optional[Callable]):
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return SentenceRecognizer(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer)
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return SentenceRecognizer(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer)
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@ -41,23 +41,6 @@ depth = 4
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DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model"]
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DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model"]
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@Language.factory(
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"span_finder",
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assigns=["doc.spans"],
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default_config={
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"threshold": 0.5,
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"model": DEFAULT_SPAN_FINDER_MODEL,
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"spans_key": DEFAULT_SPANS_KEY,
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"max_length": 25,
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"min_length": None,
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"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
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},
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default_score_weights={
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f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
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f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
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f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
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},
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)
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def make_span_finder(
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def make_span_finder(
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nlp: Language,
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nlp: Language,
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name: str,
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name: str,
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@ -32,24 +32,6 @@ PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
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DEFAULT_SPANS_KEY = "ruler"
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DEFAULT_SPANS_KEY = "ruler"
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@Language.factory(
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"future_entity_ruler",
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assigns=["doc.ents"],
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default_config={
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"phrase_matcher_attr": None,
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|
||||||
"validate": False,
|
|
||||||
"overwrite_ents": False,
|
|
||||||
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
|
|
||||||
"ent_id_sep": "__unused__",
|
|
||||||
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
|
|
||||||
},
|
|
||||||
default_score_weights={
|
|
||||||
"ents_f": 1.0,
|
|
||||||
"ents_p": 0.0,
|
|
||||||
"ents_r": 0.0,
|
|
||||||
"ents_per_type": None,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
def make_entity_ruler(
|
def make_entity_ruler(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
@ -79,30 +61,6 @@ def make_entity_ruler(
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"span_ruler",
|
|
||||||
assigns=["doc.spans"],
|
|
||||||
default_config={
|
|
||||||
"spans_key": DEFAULT_SPANS_KEY,
|
|
||||||
"spans_filter": None,
|
|
||||||
"annotate_ents": False,
|
|
||||||
"ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
|
|
||||||
"phrase_matcher_attr": None,
|
|
||||||
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
|
|
||||||
"validate": False,
|
|
||||||
"overwrite": True,
|
|
||||||
"scorer": {
|
|
||||||
"@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
|
|
||||||
"spans_key": DEFAULT_SPANS_KEY,
|
|
||||||
},
|
|
||||||
},
|
|
||||||
default_score_weights={
|
|
||||||
f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
|
|
||||||
f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
|
|
||||||
f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
|
|
||||||
f"spans_{DEFAULT_SPANS_KEY}_per_type": None,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
def make_span_ruler(
|
def make_span_ruler(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
|
||||||
|
|
@ -157,19 +157,6 @@ def build_preset_spans_suggester(spans_key: str) -> Suggester:
|
||||||
return partial(preset_spans_suggester, spans_key=spans_key)
|
return partial(preset_spans_suggester, spans_key=spans_key)
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"spancat",
|
|
||||||
assigns=["doc.spans"],
|
|
||||||
default_config={
|
|
||||||
"threshold": 0.5,
|
|
||||||
"spans_key": DEFAULT_SPANS_KEY,
|
|
||||||
"max_positive": None,
|
|
||||||
"model": DEFAULT_SPANCAT_MODEL,
|
|
||||||
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
|
|
||||||
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
|
|
||||||
},
|
|
||||||
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
|
|
||||||
)
|
|
||||||
def make_spancat(
|
def make_spancat(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
@ -222,19 +209,6 @@ def make_spancat(
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"spancat_singlelabel",
|
|
||||||
assigns=["doc.spans"],
|
|
||||||
default_config={
|
|
||||||
"spans_key": DEFAULT_SPANS_KEY,
|
|
||||||
"model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
|
|
||||||
"negative_weight": 1.0,
|
|
||||||
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
|
|
||||||
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
|
|
||||||
"allow_overlap": True,
|
|
||||||
},
|
|
||||||
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
|
|
||||||
)
|
|
||||||
def make_spancat_singlelabel(
|
def make_spancat_singlelabel(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
|
||||||
|
|
@ -35,12 +35,6 @@ subword_features = true
|
||||||
DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
|
DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"tagger",
|
|
||||||
assigns=["token.tag"],
|
|
||||||
default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!", "label_smoothing": 0.0},
|
|
||||||
default_score_weights={"tag_acc": 1.0},
|
|
||||||
)
|
|
||||||
def make_tagger(
|
def make_tagger(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
|
||||||
|
|
@ -74,27 +74,6 @@ subword_features = true
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"textcat",
|
|
||||||
assigns=["doc.cats"],
|
|
||||||
default_config={
|
|
||||||
"threshold": 0.0,
|
|
||||||
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
|
|
||||||
"scorer": {"@scorers": "spacy.textcat_scorer.v2"},
|
|
||||||
},
|
|
||||||
default_score_weights={
|
|
||||||
"cats_score": 1.0,
|
|
||||||
"cats_score_desc": None,
|
|
||||||
"cats_micro_p": None,
|
|
||||||
"cats_micro_r": None,
|
|
||||||
"cats_micro_f": None,
|
|
||||||
"cats_macro_p": None,
|
|
||||||
"cats_macro_r": None,
|
|
||||||
"cats_macro_f": None,
|
|
||||||
"cats_macro_auc": None,
|
|
||||||
"cats_f_per_type": None,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
def make_textcat(
|
def make_textcat(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
|
||||||
|
|
@ -72,27 +72,6 @@ subword_features = true
|
||||||
"""
|
"""
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"textcat_multilabel",
|
|
||||||
assigns=["doc.cats"],
|
|
||||||
default_config={
|
|
||||||
"threshold": 0.5,
|
|
||||||
"model": DEFAULT_MULTI_TEXTCAT_MODEL,
|
|
||||||
"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
|
|
||||||
},
|
|
||||||
default_score_weights={
|
|
||||||
"cats_score": 1.0,
|
|
||||||
"cats_score_desc": None,
|
|
||||||
"cats_micro_p": None,
|
|
||||||
"cats_micro_r": None,
|
|
||||||
"cats_micro_f": None,
|
|
||||||
"cats_macro_p": None,
|
|
||||||
"cats_macro_r": None,
|
|
||||||
"cats_macro_f": None,
|
|
||||||
"cats_macro_auc": None,
|
|
||||||
"cats_f_per_type": None,
|
|
||||||
},
|
|
||||||
)
|
|
||||||
def make_multilabel_textcat(
|
def make_multilabel_textcat(
|
||||||
nlp: Language,
|
nlp: Language,
|
||||||
name: str,
|
name: str,
|
||||||
|
|
|
||||||
|
|
@ -24,9 +24,6 @@ subword_features = true
|
||||||
DEFAULT_TOK2VEC_MODEL = Config().from_str(default_model_config)["model"]
|
DEFAULT_TOK2VEC_MODEL = Config().from_str(default_model_config)["model"]
|
||||||
|
|
||||||
|
|
||||||
@Language.factory(
|
|
||||||
"tok2vec", assigns=["doc.tensor"], default_config={"model": DEFAULT_TOK2VEC_MODEL}
|
|
||||||
)
|
|
||||||
def make_tok2vec(nlp: Language, name: str, model: Model) -> "Tok2Vec":
|
def make_tok2vec(nlp: Language, name: str, model: Model) -> "Tok2Vec":
|
||||||
return Tok2Vec(nlp.vocab, model, name)
|
return Tok2Vec(nlp.vocab, model, name)
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -4,11 +4,14 @@ This module centralizes registry decorations to prevent circular import issues
|
||||||
with Cython annotation changes from __future__ import annotations. Functions
|
with Cython annotation changes from __future__ import annotations. Functions
|
||||||
remain in their original locations, but decoration is moved here.
|
remain in their original locations, but decoration is moved here.
|
||||||
"""
|
"""
|
||||||
from typing import Dict, Any
|
from typing import Dict, Any, Callable, Iterable, List, Optional, Union
|
||||||
|
|
||||||
# Global flag to track if registry has been populated
|
# Global flag to track if registry has been populated
|
||||||
REGISTRY_POPULATED = False
|
REGISTRY_POPULATED = False
|
||||||
|
|
||||||
|
# Global flag to track if factories have been registered
|
||||||
|
FACTORIES_REGISTERED = False
|
||||||
|
|
||||||
def populate_registry() -> None:
|
def populate_registry() -> None:
|
||||||
"""Populate the registry with all necessary components.
|
"""Populate the registry with all necessary components.
|
||||||
|
|
||||||
|
|
@ -43,9 +46,6 @@ def populate_registry() -> None:
|
||||||
# Need to get references to the existing functions in registry by importing the function that is there
|
# Need to get references to the existing functions in registry by importing the function that is there
|
||||||
# For the registry that was previously decorated
|
# For the registry that was previously decorated
|
||||||
|
|
||||||
# Import functions for use in registry
|
|
||||||
from .scorer import get_ner_prf # Used for entity_ruler_scorer
|
|
||||||
|
|
||||||
# Import ML components that use registry
|
# Import ML components that use registry
|
||||||
from .ml.models.tok2vec import tok2vec_listener_v1, build_hash_embed_cnn_tok2vec, build_Tok2Vec_model, MultiHashEmbed, CharacterEmbed, MaxoutWindowEncoder, MishWindowEncoder, BiLSTMEncoder
|
from .ml.models.tok2vec import tok2vec_listener_v1, build_hash_embed_cnn_tok2vec, build_Tok2Vec_model, MultiHashEmbed, CharacterEmbed, MaxoutWindowEncoder, MishWindowEncoder, BiLSTMEncoder
|
||||||
|
|
||||||
|
|
@ -74,5 +74,433 @@ def populate_registry() -> None:
|
||||||
registry.architectures("spacy.MishWindowEncoder.v2")(MishWindowEncoder)
|
registry.architectures("spacy.MishWindowEncoder.v2")(MishWindowEncoder)
|
||||||
registry.architectures("spacy.TorchBiLSTMEncoder.v1")(BiLSTMEncoder)
|
registry.architectures("spacy.TorchBiLSTMEncoder.v1")(BiLSTMEncoder)
|
||||||
|
|
||||||
|
# Register factory components
|
||||||
|
register_factories()
|
||||||
|
|
||||||
# Set the flag to indicate that the registry has been populated
|
# Set the flag to indicate that the registry has been populated
|
||||||
REGISTRY_POPULATED = True
|
REGISTRY_POPULATED = True
|
||||||
|
|
||||||
|
|
||||||
|
def register_factories() -> None:
|
||||||
|
"""Register all factories with the registry.
|
||||||
|
|
||||||
|
This function registers all pipeline component factories, centralizing
|
||||||
|
the registrations that were previously done with @Language.factory decorators.
|
||||||
|
"""
|
||||||
|
global FACTORIES_REGISTERED
|
||||||
|
if FACTORIES_REGISTERED:
|
||||||
|
return
|
||||||
|
|
||||||
|
from .language import Language
|
||||||
|
|
||||||
|
# Import factory default configurations
|
||||||
|
from .pipeline.entity_linker import DEFAULT_NEL_MODEL
|
||||||
|
from .pipeline.entityruler import DEFAULT_ENT_ID_SEP
|
||||||
|
from .pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
|
||||||
|
from .pipeline.senter import DEFAULT_SENTER_MODEL
|
||||||
|
from .pipeline.morphologizer import DEFAULT_MORPH_MODEL
|
||||||
|
from .pipeline.spancat import DEFAULT_SPANCAT_MODEL, DEFAULT_SPANCAT_SINGLELABEL_MODEL, DEFAULT_SPANS_KEY
|
||||||
|
from .pipeline.span_ruler import DEFAULT_SPANS_KEY as SPAN_RULER_DEFAULT_SPANS_KEY
|
||||||
|
from .pipeline.edit_tree_lemmatizer import DEFAULT_EDIT_TREE_LEMMATIZER_MODEL
|
||||||
|
from .pipeline.textcat_multilabel import DEFAULT_MULTI_TEXTCAT_MODEL
|
||||||
|
from .pipeline.span_finder import DEFAULT_SPAN_FINDER_MODEL
|
||||||
|
from .pipeline.ner import DEFAULT_NER_MODEL
|
||||||
|
from .pipeline.dep_parser import DEFAULT_PARSER_MODEL
|
||||||
|
from .pipeline.tagger import DEFAULT_TAGGER_MODEL
|
||||||
|
from .pipeline.multitask import DEFAULT_MT_MODEL
|
||||||
|
|
||||||
|
# Import all factory functions
|
||||||
|
from .pipeline.attributeruler import make_attribute_ruler
|
||||||
|
from .pipeline.entity_linker import make_entity_linker
|
||||||
|
from .pipeline.entityruler import make_entity_ruler
|
||||||
|
from .pipeline.lemmatizer import make_lemmatizer
|
||||||
|
from .pipeline.textcat import make_textcat, DEFAULT_SINGLE_TEXTCAT_MODEL
|
||||||
|
from .pipeline.functions import make_token_splitter, make_doc_cleaner
|
||||||
|
from .pipeline.tok2vec import make_tok2vec
|
||||||
|
from .pipeline.senter import make_senter
|
||||||
|
from .pipeline.morphologizer import make_morphologizer
|
||||||
|
from .pipeline.spancat import make_spancat, make_spancat_singlelabel
|
||||||
|
from .pipeline.span_ruler import make_entity_ruler as make_span_entity_ruler, make_span_ruler
|
||||||
|
from .pipeline.edit_tree_lemmatizer import make_edit_tree_lemmatizer
|
||||||
|
from .pipeline.textcat_multilabel import make_multilabel_textcat
|
||||||
|
from .pipeline.span_finder import make_span_finder
|
||||||
|
from .pipeline.ner import make_ner, make_beam_ner
|
||||||
|
from .pipeline.dep_parser import make_parser, make_beam_parser
|
||||||
|
from .pipeline.tagger import make_tagger
|
||||||
|
from .pipeline.multitask import make_nn_labeller
|
||||||
|
from .pipeline.sentencizer import make_sentencizer
|
||||||
|
|
||||||
|
# Register factories using the same pattern as Language.factory decorator
|
||||||
|
# We use Language.factory()() pattern which exactly mimics the decorator
|
||||||
|
|
||||||
|
# attributeruler
|
||||||
|
Language.factory(
|
||||||
|
"attribute_ruler",
|
||||||
|
default_config={
|
||||||
|
"validate": False,
|
||||||
|
"scorer": {"@scorers": "spacy.attribute_ruler_scorer.v1"},
|
||||||
|
},
|
||||||
|
)(make_attribute_ruler)
|
||||||
|
|
||||||
|
# entity_linker
|
||||||
|
Language.factory(
|
||||||
|
"entity_linker",
|
||||||
|
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
|
||||||
|
assigns=["token.ent_kb_id"],
|
||||||
|
default_config={
|
||||||
|
"model": DEFAULT_NEL_MODEL,
|
||||||
|
"labels_discard": [],
|
||||||
|
"n_sents": 0,
|
||||||
|
"incl_prior": True,
|
||||||
|
"incl_context": True,
|
||||||
|
"entity_vector_length": 64,
|
||||||
|
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
|
||||||
|
"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
|
||||||
|
"generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
|
||||||
|
"overwrite": True,
|
||||||
|
"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
|
||||||
|
"use_gold_ents": True,
|
||||||
|
"candidates_batch_size": 1,
|
||||||
|
"threshold": None,
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"nel_micro_f": 1.0,
|
||||||
|
"nel_micro_r": None,
|
||||||
|
"nel_micro_p": None,
|
||||||
|
},
|
||||||
|
)(make_entity_linker)
|
||||||
|
|
||||||
|
# entity_ruler
|
||||||
|
Language.factory(
|
||||||
|
"entity_ruler",
|
||||||
|
assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
|
||||||
|
default_config={
|
||||||
|
"phrase_matcher_attr": None,
|
||||||
|
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
|
||||||
|
"validate": False,
|
||||||
|
"overwrite_ents": False,
|
||||||
|
"ent_id_sep": DEFAULT_ENT_ID_SEP,
|
||||||
|
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"ents_f": 1.0,
|
||||||
|
"ents_p": 0.0,
|
||||||
|
"ents_r": 0.0,
|
||||||
|
"ents_per_type": None,
|
||||||
|
},
|
||||||
|
)(make_entity_ruler)
|
||||||
|
|
||||||
|
# lemmatizer
|
||||||
|
Language.factory(
|
||||||
|
"lemmatizer",
|
||||||
|
assigns=["token.lemma"],
|
||||||
|
default_config={
|
||||||
|
"model": None,
|
||||||
|
"mode": "lookup",
|
||||||
|
"overwrite": False,
|
||||||
|
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={"lemma_acc": 1.0},
|
||||||
|
)(make_lemmatizer)
|
||||||
|
|
||||||
|
# textcat
|
||||||
|
Language.factory(
|
||||||
|
"textcat",
|
||||||
|
assigns=["doc.cats"],
|
||||||
|
default_config={
|
||||||
|
"threshold": 0.0,
|
||||||
|
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
|
||||||
|
"scorer": {"@scorers": "spacy.textcat_scorer.v2"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"cats_score": 1.0,
|
||||||
|
"cats_score_desc": None,
|
||||||
|
"cats_micro_p": None,
|
||||||
|
"cats_micro_r": None,
|
||||||
|
"cats_micro_f": None,
|
||||||
|
"cats_macro_p": None,
|
||||||
|
"cats_macro_r": None,
|
||||||
|
"cats_macro_f": None,
|
||||||
|
"cats_macro_auc": None,
|
||||||
|
"cats_f_per_type": None,
|
||||||
|
},
|
||||||
|
)(make_textcat)
|
||||||
|
|
||||||
|
# token_splitter
|
||||||
|
Language.factory(
|
||||||
|
"token_splitter",
|
||||||
|
default_config={"min_length": 25, "split_length": 10},
|
||||||
|
retokenizes=True,
|
||||||
|
)(make_token_splitter)
|
||||||
|
|
||||||
|
# doc_cleaner
|
||||||
|
Language.factory(
|
||||||
|
"doc_cleaner",
|
||||||
|
default_config={"attrs": {"tensor": None, "_.trf_data": None}, "silent": True},
|
||||||
|
)(make_doc_cleaner)
|
||||||
|
|
||||||
|
# tok2vec
|
||||||
|
Language.factory(
|
||||||
|
"tok2vec",
|
||||||
|
assigns=["doc.tensor"],
|
||||||
|
default_config={"model": DEFAULT_TOK2VEC_MODEL}
|
||||||
|
)(make_tok2vec)
|
||||||
|
|
||||||
|
# senter
|
||||||
|
Language.factory(
|
||||||
|
"senter",
|
||||||
|
assigns=["token.is_sent_start"],
|
||||||
|
default_config={"model": DEFAULT_SENTER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.senter_scorer.v1"}},
|
||||||
|
default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
|
||||||
|
)(make_senter)
|
||||||
|
|
||||||
|
# morphologizer
|
||||||
|
Language.factory(
|
||||||
|
"morphologizer",
|
||||||
|
assigns=["token.morph", "token.pos"],
|
||||||
|
default_config={
|
||||||
|
"model": DEFAULT_MORPH_MODEL,
|
||||||
|
"overwrite": True,
|
||||||
|
"extend": False,
|
||||||
|
"scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
|
||||||
|
"label_smoothing": 0.0
|
||||||
|
},
|
||||||
|
default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
|
||||||
|
)(make_morphologizer)
|
||||||
|
|
||||||
|
# spancat
|
||||||
|
Language.factory(
|
||||||
|
"spancat",
|
||||||
|
assigns=["doc.spans"],
|
||||||
|
default_config={
|
||||||
|
"threshold": 0.5,
|
||||||
|
"spans_key": DEFAULT_SPANS_KEY,
|
||||||
|
"max_positive": None,
|
||||||
|
"model": DEFAULT_SPANCAT_MODEL,
|
||||||
|
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
|
||||||
|
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
|
||||||
|
)(make_spancat)
|
||||||
|
|
||||||
|
# spancat_singlelabel
|
||||||
|
Language.factory(
|
||||||
|
"spancat_singlelabel",
|
||||||
|
assigns=["doc.spans"],
|
||||||
|
default_config={
|
||||||
|
"spans_key": DEFAULT_SPANS_KEY,
|
||||||
|
"model": DEFAULT_SPANCAT_SINGLELABEL_MODEL,
|
||||||
|
"negative_weight": 1.0,
|
||||||
|
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
|
||||||
|
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
|
||||||
|
"allow_overlap": True,
|
||||||
|
},
|
||||||
|
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
|
||||||
|
)(make_spancat_singlelabel)
|
||||||
|
|
||||||
|
# future_entity_ruler
|
||||||
|
Language.factory(
|
||||||
|
"future_entity_ruler",
|
||||||
|
assigns=["doc.ents"],
|
||||||
|
default_config={
|
||||||
|
"phrase_matcher_attr": None,
|
||||||
|
"validate": False,
|
||||||
|
"overwrite_ents": False,
|
||||||
|
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
|
||||||
|
"ent_id_sep": "__unused__",
|
||||||
|
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"ents_f": 1.0,
|
||||||
|
"ents_p": 0.0,
|
||||||
|
"ents_r": 0.0,
|
||||||
|
"ents_per_type": None,
|
||||||
|
},
|
||||||
|
)(make_span_entity_ruler)
|
||||||
|
|
||||||
|
# span_ruler
|
||||||
|
Language.factory(
|
||||||
|
"span_ruler",
|
||||||
|
assigns=["doc.spans"],
|
||||||
|
default_config={
|
||||||
|
"spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
|
||||||
|
"spans_filter": None,
|
||||||
|
"annotate_ents": False,
|
||||||
|
"ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
|
||||||
|
"phrase_matcher_attr": None,
|
||||||
|
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
|
||||||
|
"validate": False,
|
||||||
|
"overwrite": True,
|
||||||
|
"scorer": {
|
||||||
|
"@scorers": "spacy.overlapping_labeled_spans_scorer.v1",
|
||||||
|
"spans_key": SPAN_RULER_DEFAULT_SPANS_KEY,
|
||||||
|
},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_f": 1.0,
|
||||||
|
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_p": 0.0,
|
||||||
|
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_r": 0.0,
|
||||||
|
f"spans_{SPAN_RULER_DEFAULT_SPANS_KEY}_per_type": None,
|
||||||
|
},
|
||||||
|
)(make_span_ruler)
|
||||||
|
|
||||||
|
# trainable_lemmatizer
|
||||||
|
Language.factory(
|
||||||
|
"trainable_lemmatizer",
|
||||||
|
assigns=["token.lemma"],
|
||||||
|
requires=[],
|
||||||
|
default_config={
|
||||||
|
"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL,
|
||||||
|
"backoff": "orth",
|
||||||
|
"min_tree_freq": 3,
|
||||||
|
"overwrite": False,
|
||||||
|
"top_k": 1,
|
||||||
|
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={"lemma_acc": 1.0},
|
||||||
|
)(make_edit_tree_lemmatizer)
|
||||||
|
|
||||||
|
# textcat_multilabel
|
||||||
|
Language.factory(
|
||||||
|
"textcat_multilabel",
|
||||||
|
assigns=["doc.cats"],
|
||||||
|
default_config={
|
||||||
|
"threshold": 0.5,
|
||||||
|
"model": DEFAULT_MULTI_TEXTCAT_MODEL,
|
||||||
|
"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"cats_score": 1.0,
|
||||||
|
"cats_score_desc": None,
|
||||||
|
"cats_micro_p": None,
|
||||||
|
"cats_micro_r": None,
|
||||||
|
"cats_micro_f": None,
|
||||||
|
"cats_macro_p": None,
|
||||||
|
"cats_macro_r": None,
|
||||||
|
"cats_macro_f": None,
|
||||||
|
"cats_macro_auc": None,
|
||||||
|
"cats_f_per_type": None,
|
||||||
|
},
|
||||||
|
)(make_multilabel_textcat)
|
||||||
|
|
||||||
|
# span_finder
|
||||||
|
Language.factory(
|
||||||
|
"span_finder",
|
||||||
|
assigns=["doc.spans"],
|
||||||
|
default_config={
|
||||||
|
"threshold": 0.5,
|
||||||
|
"model": DEFAULT_SPAN_FINDER_MODEL,
|
||||||
|
"spans_key": DEFAULT_SPANS_KEY,
|
||||||
|
"max_length": 25,
|
||||||
|
"min_length": None,
|
||||||
|
"scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
|
||||||
|
f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
|
||||||
|
f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
|
||||||
|
},
|
||||||
|
)(make_span_finder)
|
||||||
|
|
||||||
|
# ner
|
||||||
|
Language.factory(
|
||||||
|
"ner",
|
||||||
|
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
|
||||||
|
default_config={
|
||||||
|
"moves": None,
|
||||||
|
"update_with_oracle_cut_size": 100,
|
||||||
|
"model": DEFAULT_NER_MODEL,
|
||||||
|
"incorrect_spans_key": None,
|
||||||
|
"scorer": {"@scorers": "spacy.ner_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
|
||||||
|
)(make_ner)
|
||||||
|
|
||||||
|
# beam_ner
|
||||||
|
Language.factory(
|
||||||
|
"beam_ner",
|
||||||
|
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
|
||||||
|
default_config={
|
||||||
|
"moves": None,
|
||||||
|
"update_with_oracle_cut_size": 100,
|
||||||
|
"model": DEFAULT_NER_MODEL,
|
||||||
|
"beam_density": 0.01,
|
||||||
|
"beam_update_prob": 0.5,
|
||||||
|
"beam_width": 32,
|
||||||
|
"incorrect_spans_key": None,
|
||||||
|
"scorer": {"@scorers": "spacy.ner_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
|
||||||
|
)(make_beam_ner)
|
||||||
|
|
||||||
|
# parser
|
||||||
|
Language.factory(
|
||||||
|
"parser",
|
||||||
|
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
|
||||||
|
default_config={
|
||||||
|
"moves": None,
|
||||||
|
"update_with_oracle_cut_size": 100,
|
||||||
|
"learn_tokens": False,
|
||||||
|
"min_action_freq": 30,
|
||||||
|
"model": DEFAULT_PARSER_MODEL,
|
||||||
|
"scorer": {"@scorers": "spacy.parser_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"dep_uas": 0.5,
|
||||||
|
"dep_las": 0.5,
|
||||||
|
"dep_las_per_type": None,
|
||||||
|
"sents_p": None,
|
||||||
|
"sents_r": None,
|
||||||
|
"sents_f": 0.0,
|
||||||
|
},
|
||||||
|
)(make_parser)
|
||||||
|
|
||||||
|
# beam_parser
|
||||||
|
Language.factory(
|
||||||
|
"beam_parser",
|
||||||
|
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
|
||||||
|
default_config={
|
||||||
|
"moves": None,
|
||||||
|
"update_with_oracle_cut_size": 100,
|
||||||
|
"learn_tokens": False,
|
||||||
|
"min_action_freq": 30,
|
||||||
|
"beam_width": 8,
|
||||||
|
"beam_density": 0.0001,
|
||||||
|
"beam_update_prob": 0.5,
|
||||||
|
"model": DEFAULT_PARSER_MODEL,
|
||||||
|
"scorer": {"@scorers": "spacy.parser_scorer.v1"},
|
||||||
|
},
|
||||||
|
default_score_weights={
|
||||||
|
"dep_uas": 0.5,
|
||||||
|
"dep_las": 0.5,
|
||||||
|
"dep_las_per_type": None,
|
||||||
|
"sents_p": None,
|
||||||
|
"sents_r": None,
|
||||||
|
"sents_f": 0.0,
|
||||||
|
},
|
||||||
|
)(make_beam_parser)
|
||||||
|
|
||||||
|
# tagger
|
||||||
|
Language.factory(
|
||||||
|
"tagger",
|
||||||
|
assigns=["token.tag"],
|
||||||
|
default_config={"model": DEFAULT_TAGGER_MODEL, "overwrite": False, "scorer": {"@scorers": "spacy.tagger_scorer.v1"}, "neg_prefix": "!", "label_smoothing": 0.0},
|
||||||
|
default_score_weights={"tag_acc": 1.0, "pos_acc": 0.0, "tag_micro_p": None, "tag_micro_r": None, "tag_micro_f": None},
|
||||||
|
)(make_tagger)
|
||||||
|
|
||||||
|
# nn_labeller
|
||||||
|
Language.factory(
|
||||||
|
"nn_labeller",
|
||||||
|
default_config={"labels": None, "target": "dep_tag_offset", "model": DEFAULT_MT_MODEL}
|
||||||
|
)(make_nn_labeller)
|
||||||
|
|
||||||
|
# sentencizer
|
||||||
|
Language.factory(
|
||||||
|
"sentencizer",
|
||||||
|
assigns=["token.is_sent_start", "doc.sents"],
|
||||||
|
default_config={"punct_chars": None, "overwrite": False, "scorer": {"@scorers": "spacy.senter_scorer.v1"}},
|
||||||
|
default_score_weights={"sents_f": 1.0, "sents_p": 0.0, "sents_r": 0.0},
|
||||||
|
)(make_sentencizer)
|
||||||
|
|
||||||
|
# Set the flag to indicate that all factories have been registered
|
||||||
|
FACTORIES_REGISTERED = True
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue
Block a user