original_docstring: 
      Measure the semantic similarity between one spacy Doc, Span, Token, or Lexeme
      and another like object using the cosine distance between the objects' (average)
      word2vec vectors.
  
      Args:
          obj1
          obj2
  
      Returns
          Similarity between ``obj1`` and ``obj2`` in the interval [0.0, 1.0],
          where larger values correspond to more similar objects
      
docstring: Measure the semantic similarity between one spacy Doc, Span, Token, or Lexeme
  and another like object using the cosine distance between the objects' (average)
  word2vec vectors.
  
  obj1
  obj2
  
  
docstring_tokens: Measure the semantic similarity between one spacy Doc Span Token or Lexeme and another like object using the cosine distance between the objects ' ( average ) word2vec vectors . obj1 obj2
code: def word2vec(obj1: Union[Doc, Span, Token], obj2: Union[Doc, Span, Token]) -> float:
      """
      Measure the semantic similarity between one spacy Doc, Span, Token, or Lexeme
      and another like object using the cosine distance between the objects' (average)
      word2vec vectors.
  
      Args:
          obj1
          obj2
  
      Returns
          Similarity between ``obj1`` and ``obj2`` in the interval [0.0, 1.0],
          where larger values correspond to more similar objects
      """
      return obj1.similarity(obj2)
code_tokens: def word2vec ( obj1 : Union [ Doc , Span , Token ] , obj2 : Union [ Doc , Span , Token ] ) -> float : return obj1 . similarity ( obj2 )
short_docstring: Measure the semantic similarity between one spacy Doc, Span, Token, or Lexeme
  and another like object using the cosine distance between the objects' (average)
  word2vec vectors.
short_docstring_tokens: Measure the semantic similarity between one spacy Doc Span Token or Lexeme and another like object using the cosine distance between the objects ' ( average ) word2vec vectors .
comment: """
      Measure the semantic similarity between one spacy Doc, Span, Token, or Lexeme
      and another like object using the cosine distance between the objects' (average)
      word2vec vectors.
  
      Args:
          obj1
          obj2
  
      Returns
          Similarity between ``obj1`` and ``obj2`` in the interval [0.0, 1.0],
          where larger values correspond to more similar objects
      """