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Update typing for get_candidates_all().
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@ -118,13 +118,13 @@ def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
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def get_candidates_all(
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kb: KnowledgeBase, mentions: Generator[Iterable[Span], None, None]
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kb: KnowledgeBase, mentions: Iterator[Iterable[Span]]
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) -> Iterator[Iterable[Iterable[Candidate]]]:
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"""
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Return candidate entities for the given mentions and fetching appropriate entries from the index.
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kb (KnowledgeBase): Knowledge base to query.
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mention (Generator[Iterable[Span]]): Entity mentions per document for which to identify candidates.
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RETURNS (Generator[Iterable[Iterable[Candidate]]]): Identified candidates per document.
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mention (Iterator[Iterable[Span]]): Entity mentions per document for which to identify candidates.
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RETURNS (Iterator[Iterable[Iterable[Candidate]]]): Identified candidates per document.
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"""
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return kb.get_candidates_all(mentions)
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@ -6,7 +6,6 @@ from typing import (
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Union,
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List,
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Any,
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Generator,
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Iterator,
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)
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from thinc.types import Floats2d
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@ -88,7 +87,7 @@ def make_entity_linker(
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entity_vector_length: int,
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get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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get_candidates_all: Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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[KnowledgeBase, Iterator[Iterable[Span]]],
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Iterator[Iterable[Iterable[Candidate]]],
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],
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generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
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@ -110,11 +109,8 @@ def make_entity_linker(
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entity_vector_length (int): Size of encoding vectors in the KB.
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get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
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produces a list of candidates, given a certain knowledge base and a textual mention.
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get_candidates_all (
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Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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Iterator[Iterable[Iterable[Candidate]]]
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]): Function that produces a list of candidates per document, given a certain knowledge base and several textual
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get_candidates_all (Callable[[KnowledgeBase, Iterator[Iterable[Span]]], Iterator[Iterable[Iterable[Candidate]]]]):
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Function that produces a list of candidates per document, given a certain knowledge base and several textual
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documents with textual mentions.
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generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
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scorer (Optional[Callable]): The scoring method.
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@ -192,7 +188,7 @@ class EntityLinker(TrainablePipe):
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entity_vector_length: int,
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get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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get_candidates_all: Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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[KnowledgeBase, Iterator[Iterable[Span]]],
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Iterator[Iterable[Iterable[Candidate]]],
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],
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generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
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@ -215,12 +211,9 @@ class EntityLinker(TrainablePipe):
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entity_vector_length (int): Size of encoding vectors in the KB.
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get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
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produces a list of candidates, given a certain knowledge base and a textual mention.
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get_candidates_all (
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Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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Iterator[Iterable[Iterable[Candidate]]]
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]): Function that produces a list of candidates per document, given a certain knowledge base and several
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textual documents with textual mentions.
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get_candidates_all (Callable[[KnowledgeBase, Iterator[Iterable[Span]]], Iterator[Iterable[Iterable[Candidate]]]]):
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Function that produces a list of candidates per document, given a certain knowledge base and several textual
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documents with textual mentions.
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generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
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scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
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use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
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