code: def create(text,score,prompt_string, dump_data=False):
      
  
      if dump_data:
          dump_input_data(text, score)
  
      algorithm = select_algorithm(score)
      #Initialize a results dictionary to return
      results = {'errors': [],'success' : False, 'cv_kappa' : 0, 'cv_mean_absolute_error': 0,
                 'feature_ext' : "", 'classifier' : "", 'algorithm' : algorithm,
                 'score' : score, 'text' : text, 'prompt' : prompt_string}
  
      if len(text)!=len(score):
          msg = "Target and text lists must be same length."
          results['errors'].append(msg)
          log.exception(msg)
          return results
  
      try:
          #Create an essay set object that encapsulates all the essays and alternate representations (tokens, etc)
          e_set = model_creator.create_essay_set(text, score, prompt_string)
      except:
          msg = "essay set creation failed."
          results['errors'].append(msg)
          log.exception(msg)
      try:
          #Gets features from the essay set and computes error
          feature_ext, classifier, cv_error_results = model_creator.extract_features_and_generate_model(e_set, algorithm = algorithm)
          results['cv_kappa']=cv_error_results['kappa']
          results['cv_mean_absolute_error']=cv_error_results['mae']
          results['feature_ext']=feature_ext
          results['classifier']=classifier
          results['algorithm'] = algorithm
          results['success']=True
      except:
          msg = "feature extraction and model creation failed."
          results['errors'].append(msg)
          log.exception(msg)
  
      return results
docs: Creates a machine learning model from input text, associated scores, a prompt, and a path to the model
      TODO: Remove model path argument, it is needed for now to support legacy code
      text - A list of strings containing the text of the essays
      score - a list of integers containing score values
      prompt_string - the common prompt for the set of essays