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https://github.com/explosion/spaCy.git
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consistently use registry as callable
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212f0e779e
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@ -8,7 +8,7 @@ from ...kb import KnowledgeBase, Candidate, get_candidates
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from ...vocab import Vocab
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@registry.architectures.register("spacy.EntityLinker.v1")
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@registry.architectures("spacy.EntityLinker.v1")
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def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
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with Model.define_operators({">>": chain, "**": clone}):
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token_width = tok2vec.get_dim("nO")
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@ -25,7 +25,7 @@ def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
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return model
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@registry.misc.register("spacy.KBFromFile.v1")
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@registry.misc("spacy.KBFromFile.v1")
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def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
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def kb_from_file(vocab):
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kb = KnowledgeBase(vocab, entity_vector_length=1)
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@ -35,7 +35,7 @@ def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
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return kb_from_file
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@registry.misc.register("spacy.EmptyKB.v1")
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@registry.misc("spacy.EmptyKB.v1")
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def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
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def empty_kb_factory(vocab):
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return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
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@ -43,6 +43,6 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
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return empty_kb_factory
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@registry.misc.register("spacy.CandidateGenerator.v1")
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@registry.misc("spacy.CandidateGenerator.v1")
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def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
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return get_candidates
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@ -16,7 +16,7 @@ if TYPE_CHECKING:
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from ...tokens import Doc # noqa: F401
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@registry.architectures.register("spacy.PretrainVectors.v1")
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@registry.architectures("spacy.PretrainVectors.v1")
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def create_pretrain_vectors(
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maxout_pieces: int, hidden_size: int, loss: str
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) -> Callable[["Vocab", Model], Model]:
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@ -40,7 +40,7 @@ def create_pretrain_vectors(
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return create_vectors_objective
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@registry.architectures.register("spacy.PretrainCharacters.v1")
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@registry.architectures("spacy.PretrainCharacters.v1")
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def create_pretrain_characters(
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maxout_pieces: int, hidden_size: int, n_characters: int
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) -> Callable[["Vocab", Model], Model]:
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@ -10,7 +10,7 @@ from ..tb_framework import TransitionModel
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from ...tokens import Doc
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@registry.architectures.register("spacy.TransitionBasedParser.v1")
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@registry.architectures("spacy.TransitionBasedParser.v1")
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def transition_parser_v1(
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tok2vec: Model[List[Doc], List[Floats2d]],
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state_type: Literal["parser", "ner"],
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@ -31,7 +31,7 @@ def transition_parser_v1(
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)
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@registry.architectures.register("spacy.TransitionBasedParser.v2")
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@registry.architectures("spacy.TransitionBasedParser.v2")
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def transition_parser_v2(
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tok2vec: Model[List[Doc], List[Floats2d]],
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state_type: Literal["parser", "ner"],
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@ -6,7 +6,7 @@ from ...util import registry
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from ...tokens import Doc
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@registry.architectures.register("spacy.Tagger.v1")
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@registry.architectures("spacy.Tagger.v1")
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def build_tagger_model(
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tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None
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) -> Model[List[Doc], List[Floats2d]]:
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@ -15,7 +15,7 @@ from ...tokens import Doc
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from .tok2vec import get_tok2vec_width
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@registry.architectures.register("spacy.TextCatCNN.v1")
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@registry.architectures("spacy.TextCatCNN.v1")
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def build_simple_cnn_text_classifier(
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tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
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) -> Model[List[Doc], Floats2d]:
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@ -41,7 +41,7 @@ def build_simple_cnn_text_classifier(
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return model
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@registry.architectures.register("spacy.TextCatBOW.v1")
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@registry.architectures("spacy.TextCatBOW.v1")
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def build_bow_text_classifier(
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exclusive_classes: bool,
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ngram_size: int,
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@ -60,7 +60,7 @@ def build_bow_text_classifier(
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return model
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@registry.architectures.register("spacy.TextCatEnsemble.v2")
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@registry.architectures("spacy.TextCatEnsemble.v2")
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def build_text_classifier_v2(
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tok2vec: Model[List[Doc], List[Floats2d]],
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linear_model: Model[List[Doc], Floats2d],
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@ -112,7 +112,7 @@ def init_ensemble_textcat(model, X, Y) -> Model:
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return model
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@registry.architectures.register("spacy.TextCatLowData.v1")
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@registry.architectures("spacy.TextCatLowData.v1")
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def build_text_classifier_lowdata(
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width: int, dropout: Optional[float], nO: Optional[int] = None
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) -> Model[List[Doc], Floats2d]:
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@ -14,7 +14,7 @@ from ...pipeline.tok2vec import Tok2VecListener
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from ...attrs import intify_attr
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@registry.architectures.register("spacy.Tok2VecListener.v1")
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@registry.architectures("spacy.Tok2VecListener.v1")
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def tok2vec_listener_v1(width: int, upstream: str = "*"):
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tok2vec = Tok2VecListener(upstream_name=upstream, width=width)
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return tok2vec
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@ -31,7 +31,7 @@ def get_tok2vec_width(model: Model):
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return nO
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@registry.architectures.register("spacy.HashEmbedCNN.v1")
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@registry.architectures("spacy.HashEmbedCNN.v1")
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def build_hash_embed_cnn_tok2vec(
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*,
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width: int,
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@ -87,7 +87,7 @@ def build_hash_embed_cnn_tok2vec(
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)
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@registry.architectures.register("spacy.Tok2Vec.v2")
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@registry.architectures("spacy.Tok2Vec.v2")
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def build_Tok2Vec_model(
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embed: Model[List[Doc], List[Floats2d]],
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encode: Model[List[Floats2d], List[Floats2d]],
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@ -108,7 +108,7 @@ def build_Tok2Vec_model(
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return tok2vec
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@registry.architectures.register("spacy.MultiHashEmbed.v1")
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@registry.architectures("spacy.MultiHashEmbed.v1")
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def MultiHashEmbed(
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width: int,
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attrs: List[Union[str, int]],
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@ -182,7 +182,7 @@ def MultiHashEmbed(
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return model
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@registry.architectures.register("spacy.CharacterEmbed.v1")
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@registry.architectures("spacy.CharacterEmbed.v1")
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def CharacterEmbed(
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width: int,
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rows: int,
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@ -255,7 +255,7 @@ def CharacterEmbed(
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return model
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@registry.architectures.register("spacy.MaxoutWindowEncoder.v2")
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@registry.architectures("spacy.MaxoutWindowEncoder.v2")
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def MaxoutWindowEncoder(
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width: int, window_size: int, maxout_pieces: int, depth: int
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) -> Model[List[Floats2d], List[Floats2d]]:
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@ -287,7 +287,7 @@ def MaxoutWindowEncoder(
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return with_array(model, pad=receptive_field)
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@registry.architectures.register("spacy.MishWindowEncoder.v2")
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@registry.architectures("spacy.MishWindowEncoder.v2")
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def MishWindowEncoder(
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width: int, window_size: int, depth: int
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) -> Model[List[Floats2d], List[Floats2d]]:
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@ -310,7 +310,7 @@ def MishWindowEncoder(
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return with_array(model)
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@registry.architectures.register("spacy.TorchBiLSTMEncoder.v1")
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@registry.architectures("spacy.TorchBiLSTMEncoder.v1")
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def BiLSTMEncoder(
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width: int, depth: int, dropout: float
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) -> Model[List[Floats2d], List[Floats2d]]:
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@ -230,7 +230,7 @@ def test_el_pipe_configuration(nlp):
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def get_lowercased_candidates(kb, span):
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return kb.get_alias_candidates(span.text.lower())
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@registry.misc.register("spacy.LowercaseCandidateGenerator.v1")
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@registry.misc("spacy.LowercaseCandidateGenerator.v1")
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def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
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return get_lowercased_candidates
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@ -160,7 +160,7 @@ subword_features = false
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"""
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@registry.architectures.register("my_test_parser")
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@registry.architectures("my_test_parser")
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def my_parser():
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tok2vec = build_Tok2Vec_model(
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MultiHashEmbed(
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@ -108,7 +108,7 @@ def test_serialize_subclassed_kb():
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super().__init__(vocab, entity_vector_length)
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self.custom_field = custom_field
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@registry.misc.register("spacy.CustomKB.v1")
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@registry.misc("spacy.CustomKB.v1")
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def custom_kb(
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entity_vector_length: int, custom_field: int
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) -> Callable[["Vocab"], KnowledgeBase]:
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@ -4,12 +4,12 @@ from thinc.api import Linear
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from catalogue import RegistryError
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@registry.architectures.register("my_test_function")
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def test_get_architecture():
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@registry.architectures("my_test_function")
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def create_model(nr_in, nr_out):
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return Linear(nr_in, nr_out)
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def test_get_architecture():
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arch = registry.architectures.get("my_test_function")
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assert arch is create_model
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with pytest.raises(RegistryError):
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@ -27,7 +27,7 @@ def test_readers():
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factory = "textcat"
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"""
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@registry.readers.register("myreader.v1")
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@registry.readers("myreader.v1")
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def myreader() -> Dict[str, Callable[[Language, str], Iterable[Example]]]:
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annots = {"cats": {"POS": 1.0, "NEG": 0.0}}
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@ -15,7 +15,7 @@ next: /usage/projects
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> ```python
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> from thinc.api import Model, chain
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>
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> @spacy.registry.architectures.register("model.v1")
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> @spacy.registry.architectures("model.v1")
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> def build_model(width: int, classes: int) -> Model:
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> tok2vec = build_tok2vec(width)
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> output_layer = build_output_layer(width, classes)
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@ -563,7 +563,7 @@ matrix** (~~Floats2d~~) of predictions:
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```python
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### The model architecture
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@spacy.registry.architectures.register("rel_model.v1")
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@spacy.registry.architectures("rel_model.v1")
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def create_relation_model(...) -> Model[List[Doc], Floats2d]:
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model = ... # 👈 model will go here
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return model
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@ -589,7 +589,7 @@ transforms the instance tensor into a final tensor holding the predictions:
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```python
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### The model architecture {highlight="6"}
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@spacy.registry.architectures.register("rel_model.v1")
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@spacy.registry.architectures("rel_model.v1")
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def create_relation_model(
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create_instance_tensor: Model[List[Doc], Floats2d],
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classification_layer: Model[Floats2d, Floats2d],
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@ -613,7 +613,7 @@ The `classification_layer` could be something like a
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```python
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### The classification layer
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@spacy.registry.architectures.register("rel_classification_layer.v1")
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@spacy.registry.architectures("rel_classification_layer.v1")
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def create_classification_layer(
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nO: int = None, nI: int = None
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) -> Model[Floats2d, Floats2d]:
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@ -650,7 +650,7 @@ that has the full implementation.
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```python
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### The layer that creates the instance tensor
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@spacy.registry.architectures.register("rel_instance_tensor.v1")
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@spacy.registry.architectures("rel_instance_tensor.v1")
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def create_tensors(
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tok2vec: Model[List[Doc], List[Floats2d]],
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pooling: Model[Ragged, Floats2d],
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@ -731,7 +731,7 @@ are within a **maximum distance** (in number of tokens) of each other:
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```python
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### Candidate generation
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@spacy.registry.misc.register("rel_instance_generator.v1")
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@spacy.registry.misc("rel_instance_generator.v1")
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def create_instances(max_length: int) -> Callable[[Doc], List[Tuple[Span, Span]]]:
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def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
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candidates = []
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