mirror of
https://github.com/explosion/spaCy.git
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5f8a398bb9
* Add span_id to Span.char_span, update Doc/Span.char_span docs `Span.char_span(id=)` should be removed in the future. * Also use Union[int, str] in Doc docstring
133 lines
3.6 KiB
Python
133 lines
3.6 KiB
Python
from typing import Callable, Protocol, Iterator, Optional, Union, Tuple, Any, overload
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from thinc.types import Floats1d, Ints2d, FloatsXd
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from .doc import Doc
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from .token import Token
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from .underscore import Underscore
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from ..lexeme import Lexeme
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from ..vocab import Vocab
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class SpanMethod(Protocol):
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def __call__(self: Span, *args: Any, **kwargs: Any) -> Any: ... # type: ignore[misc]
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class Span:
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@classmethod
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def set_extension(
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cls,
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name: str,
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default: Optional[Any] = ...,
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getter: Optional[Callable[[Span], Any]] = ...,
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setter: Optional[Callable[[Span, Any], None]] = ...,
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method: Optional[SpanMethod] = ...,
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force: bool = ...,
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) -> None: ...
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@classmethod
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def get_extension(
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cls, name: str
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) -> Tuple[
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Optional[Any],
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Optional[SpanMethod],
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Optional[Callable[[Span], Any]],
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Optional[Callable[[Span, Any], None]],
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]: ...
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@classmethod
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def has_extension(cls, name: str) -> bool: ...
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@classmethod
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def remove_extension(
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cls, name: str
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) -> Tuple[
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Optional[Any],
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Optional[SpanMethod],
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Optional[Callable[[Span], Any]],
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Optional[Callable[[Span, Any], None]],
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]: ...
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def __init__(
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self,
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doc: Doc,
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start: int,
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end: int,
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label: Union[str, int] = ...,
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vector: Optional[Floats1d] = ...,
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vector_norm: Optional[float] = ...,
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kb_id: Union[str, int] = ...,
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span_id: Union[str, int] = ...,
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) -> None: ...
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def __richcmp__(self, other: Span, op: int) -> bool: ...
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def __hash__(self) -> int: ...
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def __len__(self) -> int: ...
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def __repr__(self) -> str: ...
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@overload
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def __getitem__(self, i: int) -> Token: ...
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@overload
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def __getitem__(self, i: slice) -> Span: ...
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def __iter__(self) -> Iterator[Token]: ...
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@property
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def _(self) -> Underscore: ...
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def as_doc(self, *, copy_user_data: bool = ...) -> Doc: ...
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def get_lca_matrix(self) -> Ints2d: ...
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def similarity(self, other: Union[Doc, Span, Token, Lexeme]) -> float: ...
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@property
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def doc(self) -> Doc: ...
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@property
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def vocab(self) -> Vocab: ...
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@property
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def sent(self) -> Span: ...
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@property
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def ents(self) -> Tuple[Span]: ...
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@property
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def has_vector(self) -> bool: ...
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@property
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def vector(self) -> Floats1d: ...
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@property
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def vector_norm(self) -> float: ...
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@property
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def tensor(self) -> FloatsXd: ...
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@property
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def sentiment(self) -> float: ...
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@property
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def text(self) -> str: ...
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@property
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def text_with_ws(self) -> str: ...
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@property
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def noun_chunks(self) -> Iterator[Span]: ...
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@property
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def root(self) -> Token: ...
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def char_span(
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self,
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start_idx: int,
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end_idx: int,
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label: Union[int, str] = ...,
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kb_id: Union[int, str] = ...,
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vector: Optional[Floats1d] = ...,
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id: Union[int, str] = ...,
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alignment_mode: str = ...,
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span_id: Union[int, str] = ...,
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) -> Span: ...
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@property
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def conjuncts(self) -> Tuple[Token]: ...
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@property
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def lefts(self) -> Iterator[Token]: ...
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@property
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def rights(self) -> Iterator[Token]: ...
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@property
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def n_lefts(self) -> int: ...
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@property
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def n_rights(self) -> int: ...
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@property
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def subtree(self) -> Iterator[Token]: ...
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start: int
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end: int
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start_char: int
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end_char: int
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label: int
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kb_id: int
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id: int
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ent_id: int
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ent_id_: str
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@property
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def orth_(self) -> str: ...
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@property
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def lemma_(self) -> str: ...
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label_: str
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kb_id_: str
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id_: str
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