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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
from pathlib import Path
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from typing import Optional, Callable, Iterable
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from thinc.api import chain, clone, list2ragged, reduce_mean, residual
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from thinc.api import Model, Maxout, Linear
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from ...util import registry
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from ...kb import KnowledgeBase, Candidate, get_candidates
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from ...vocab import Vocab
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from ...tokens import Span
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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.maybe_get_dim("nO")
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output_layer = Linear(nO=nO, nI=token_width)
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model = (
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tok2vec
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>> list2ragged()
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>> reduce_mean()
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>> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
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>> output_layer
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)
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model.set_ref("output_layer", output_layer)
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model.set_ref("tok2vec", tok2vec)
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return model
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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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kb.from_disk(kb_path)
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return kb
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return kb_from_file
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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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return empty_kb_factory
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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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