  @plac.annotations(
      train_loc=("Location of training file or directory"),
      dev_loc=("Location of development file or directory"),
      corruption_level=("Amount of noise to add to training data", "option", "c", float),
      gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
      out_loc=("Out location", "option", "o", str),
      n_sents=("Number of training sentences", "option", "n", int),
      n_iter=("Number of training iterations", "option", "i", int),
 
</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):
    model_dir=("Location of output model directory",),
    eval_only=("Skip training, and only evaluate", "flag", "e", bool),
  @plac.annotations(
      train_loc=("Location of training file or directory"),
      dev_loc=("Location of development file or directory"),
    model_dir=("Location of output model directory",),
    eval_only=("Skip training, and only evaluate", "flag", "e", bool),
      corruption_level=("Amount of noise to add to training data", "option", "c", float),
      gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
      out_loc=("Out location", "option", "o", str),
      n_sents=("Number of training sentences", "option", "n", int),
      n_iter=("Number of training iterations", "option", "i", int),
keep keep add keep keep keep keep keep
 * Add more options to bin/parser/train 
bin/parser/train.py