      nlp.tagger.model.end_training()
      nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
  
  
  def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False):
      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 
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
             beam_width=None):
      nlp.tagger.model.end_training()
      nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
  
  
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
             beam_width=None):
      nlp = Language(data_dir=model_dir)
      scorer = Scorer()
      for raw_text, sents in gold_tuples:
          if gold_preproc:
              raw_text = None
keep keep keep keep replace keep keep keep keep keep
 * Add more options to bin/parser/train 
bin/parser/train.py