               beam_width=None):
      nlp = Language(data_dir=model_dir)
      scorer = Scorer()
      for raw_text, sents in gold_tuples:
          if gold_preproc:
              raw_text = None
 
</s> remove     model_dir=("Location of output model directory",),
 </s> add  </s> remove def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False):
 </s> add def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
             beam_width=None):
    if beam_width is not None:
        nlp.parser.cfg.beam_width = beam_width
               beam_width=None):
      nlp = Language(data_dir=model_dir)
    if beam_width is not None:
        nlp.parser.cfg.beam_width = beam_width
      scorer = Scorer()
      for raw_text, sents in gold_tuples:
          if gold_preproc:
              raw_text = None
keep add keep keep keep keep
 * Add more options to bin/parser/train 
bin/parser/train.py