(self, doclikes: Iterable[types.DocLike])
Convert a sequence of spaCy Docs or Spans into an ordered, nested sequence
        of terms as strings.

        Args:
            doclikes

        Yields:
            Ordered sequence of terms as strings for next Doc or Span.
Convert a sequence of spaCy Docs or Spans into an ordered, nested sequence
        of terms as strings.
Convert a sequence of spaCy Docs or Spans into an ordered nested sequence of terms as strings .
def transform(self, doclikes: Iterable[types.DocLike]) -> Iterable[Tuple[str, ...]]:
        """
        Convert a sequence of spaCy Docs or Spans into an ordered, nested sequence
        of terms as strings.

        Args:
            doclikes

        Yields:
            Ordered sequence of terms as strings for next Doc or Span.
        """
        normalize_ = self.normalize
        for doclike in doclikes:
            terms = itertoolz.concat(tokenizer(doclike) for tokenizer in self.tokenizers)
            if self.dedupe is True:
                terms = itertoolz.unique(terms, lambda span: (span.start, span.end))
            yield tuple(normalize_(term) for term in terms)
def transform ( self , doclikes : Iterable [ types . DocLike ] ) -> Iterable [ Tuple [ str , ... ] ] : normalize_ = self . normalize for doclike in doclikes : terms = itertoolz . concat ( tokenizer ( doclike ) for tokenizer in self . tokenizers ) if self . dedupe is True : terms = itertoolz . unique ( terms , lambda span : ( span . start , span . end ) ) yield tuple ( normalize_ ( term ) for term in terms )