original_docstring: Given a fastText vector file, output word vectors into a DataFrame where
      key: word token (string), value: word vector (numpy array).
      NOTE: First row in a fastText vector file holds the number of tokens and 
      vector length.
      
docstring: Given a fastText vector file, output word vectors into a DataFrame where
  key: word token (string), value: word vector (numpy array).
  NOTE: First row in a fastText vector file holds the number of tokens and
  vector length.
docstring_tokens: Given a fastText vector file output word vectors into a DataFrame where key : word token ( string ) value : word vector ( numpy array ) . NOTE : First row in a fastText vector file holds the number of tokens and vector length .
code: def build_wordvec_index(vec_filename, export_path):
      """Given a fastText vector file, output word vectors into a DataFrame where
      key: word token (string), value: word vector (numpy array).
      NOTE: First row in a fastText vector file holds the number of tokens and 
      vector length.
      """
      with open(vec_filename, 'r') as vec_file:
          dimensions = [int(dim) for dim in vec_file.readline().split()]
          print('wordvec matrix dimensions:', dimensions)
          vec_matrix = np.zeros(dimensions, dtype=np.float32)
          index_tokens = []
          for idx, row in enumerate(vec_file):
              token, vector = row.split(' ', 1)
              vec_matrix[idx] = np.fromstring(vector, sep=' ')
              index_tokens.append(token)
      pd.DataFrame(data=vec_matrix, index=index_tokens).to_pickle(export_path)
      print('wordvec index saved in', os.path.realpath(export_path))
code_tokens: def build_wordvec_index ( vec_filename , export_path ) : with open ( vec_filename , 'r' ) as vec_file : dimensions = [ int ( dim ) for dim in vec_file . readline ( ) . split ( ) ] print ( 'wordvec matrix dimensions:' , dimensions ) vec_matrix = np . zeros ( dimensions , dtype = np . float32 ) index_tokens = [ ] for idx , row in enumerate ( vec_file ) : token , vector = row . split ( ' ' , 1 ) vec_matrix [ idx ] = np . fromstring ( vector , sep = ' ' ) index_tokens . append ( token ) pd . DataFrame ( data = vec_matrix , index = index_tokens ) . to_pickle ( export_path ) print ( 'wordvec index saved in' , os . path . realpath ( export_path ) )
short_docstring: Given a fastText vector file, output word vectors into a DataFrame where
  key: word token (string), value: word vector (numpy array).
short_docstring_tokens: Given a fastText vector file output word vectors into a DataFrame where key : word token ( string ) value : word vector ( numpy array ) .
comment: """Given a fastText vector file, output word vectors into a DataFrame where
      key: word token (string), value: word vector (numpy array).
      NOTE: First row in a fastText vector file holds the number of tokens and 
      vector length.
      """