original_docstring: 
          Creates dictionary containing the most significant words appearing in the training data.
  
          Note that dictionary keys are token ID's, values are the words/tokens themselves.
  
          Keyword Arguments:
          num_keep -- The number of top (most common) words stored in dictionary.
          max_proportion -- The maximum proportion of the training data a word can appear in.
  
          Return Value:
          self.training_dict -- Dictionary with top words (words meeting requirements set by keyword args).
          
docstring: Creates dictionary containing the most significant words appearing in the training data.
  Note that dictionary keys are token ID's, values are the words/tokens themselves.
  
  Keyword Arguments:
  num_keep -- The number of top (most common) words stored in dictionary.
  max_proportion -- The maximum proportion of the training data a word can appear in.
  
  Return Value:
  self.training_dict -- Dictionary with top words (words meeting requirements set by keyword args).
docstring_tokens: Creates dictionary containing the most significant words appearing in the training data . Note that dictionary keys are token ID ' s values are the words / tokens themselves . Keyword Arguments : num_keep -- The number of top ( most common ) words stored in dictionary . max_proportion -- The maximum proportion of the training data a word can appear in . Return Value : self . training_dict -- Dictionary with top words ( words meeting requirements set by keyword args ) .
code: def create_training_dict(self, max_proportion: int, num_keep: int) -> dict:
          """
          Creates dictionary containing the most significant words appearing in the training data.
  
          Note that dictionary keys are token ID's, values are the words/tokens themselves.
  
          Keyword Arguments:
          num_keep -- The number of top (most common) words stored in dictionary.
          max_proportion -- The maximum proportion of the training data a word can appear in.
  
          Return Value:
          self.training_dict -- Dictionary with top words (words meeting requirements set by keyword args).
          """
          assert self.processed_projects
  
          self.training_dict = gensim.corpora.Dictionary(self.processed_projects)
          self.training_dict.filter_extremes(no_above=max_proportion, keep_n=num_keep)
          return self.training_dict
code_tokens: def create_training_dict ( self , max_proportion : int , num_keep : int ) -> dict : assert self . processed_projects self . training_dict = gensim . corpora . Dictionary ( self . processed_projects ) self . training_dict . filter_extremes ( no_above = max_proportion , keep_n = num_keep ) return self . training_dict
short_docstring: Creates dictionary containing the most significant words appearing in the training data.
short_docstring_tokens: Creates dictionary containing the most significant words appearing in the training data .
comment: """
          Creates dictionary containing the most significant words appearing in the training data.
  
          Note that dictionary keys are token ID's, values are the words/tokens themselves.
  
          Keyword Arguments:
          num_keep -- The number of top (most common) words stored in dictionary.
          max_proportion -- The maximum proportion of the training data a word can appear in.
  
          Return Value:
          self.training_dict -- Dictionary with top words (words meeting requirements set by keyword args).
          """