original_docstring: Loads the embedding vectors of the vocabulary strings from disk.
  
      Returns:
          vectors (array): a numpy array which as much rows as words in the vocabulary.
              the number of columns is equal to the vector's dimensions (300 in this case).
  
          default_vector (array): a numpy array of size as `vectors`.
              this vector is used for out-of-vocabulary tokens.
      
docstring: Loads the embedding vectors of the vocabulary strings from disk.
docstring_tokens: Loads the embedding vectors of the vocabulary strings from disk .
code: def _load_vectors():
      """Loads the embedding vectors of the vocabulary strings from disk.
  
      Returns:
          vectors (array): a numpy array which as much rows as words in the vocabulary.
              the number of columns is equal to the vector's dimensions (300 in this case).
  
          default_vector (array): a numpy array of size as `vectors`.
              this vector is used for out-of-vocabulary tokens.
      """
  
      # Get the path to the file where vectors are stored
      vectors_path = os.path.join(DATA_PATH, "vectors")
  
      # Unpickle the vectors
      with open(vectors_path, "rb") as vectors_file:
          vectors = pickle.load(vectors_file)
  
      # Create a default vector that is returned
      # when an out-of-vocabulary token is encountered
      default_vector = np.zeros(vectors.shape[1], dtype=vectors.dtype)
  
      return vectors, default_vector
code_tokens: def _load_vectors ( ) : vectors_path = os . path . join ( DATA_PATH , "vectors" ) with open ( vectors_path , "rb" ) as vectors_file : vectors = pickle . load ( vectors_file ) default_vector = np . zeros ( vectors . shape [ 1 ] , dtype = vectors . dtype ) return vectors , default_vector
short_docstring: Loads the embedding vectors of the vocabulary strings from disk.
short_docstring_tokens: Loads the embedding vectors of the vocabulary strings from disk .
comment: """Loads the embedding vectors of the vocabulary strings from disk.
  
      Returns:
          vectors (array): a numpy array which as much rows as words in the vocabulary.
              the number of columns is equal to the vector's dimensions (300 in this case).
  
          default_vector (array): a numpy array of size as `vectors`.
              this vector is used for out-of-vocabulary tokens.
      """$SEP$# Get the path to the file where vectors are stored$SEP$# Unpickle the vectors$SEP$# Create a default vector that is returned$SEP$# when an out-of-vocabulary token is encountered