old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from nltk.tag.stanford import StanfordPOSTagger
  
  from .config import DATA_DIR_NAME, PATH_TO_DATA_DIR
  from .files import TrainingFile, write_to_directory
  from .tag import FilePair
  
  class TestSuite(object):
      """Collection of files for training/testing part-of-speech taggers. """
  
      def __init__(self, tagged_file, separator='_',
                   number_of_groups=10, ws_delim=True, by_morphemes=False):
          """Initialize the test suite."""
          self.tagged_file = tagged_file
          self.sep = separator
          self.number_of_groups = number_of_groups
          self.ws_delim = ws_delim
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from nltk.tag.stanford import StanfordPOSTagger
  
  from .config import DATA_DIR_NAME, PATH_TO_DATA_DIR
  from .files import TrainingFile, write_to_directory
  from .tag import FilePair
  
  class TaggerTester(object):
      """Collection of files for training/testing part-of-speech taggers. """
  
      def __init__(self):
          """Initialize the test suite."""
          pass
  
  
  class SentencePair(object):
      """Pair of sentences: one tagged by hand, one by a POS tagger."""
  
      def __init__(self, hand_tagged_sentence, auto_tagged_sentence,
                   separator='_'):
          """Initialize the object.
  
             Parameters
             ----------
               hand_tagged_sentence (unicode / str) : a sentence which has
                 been tagged by hand (i.e., it belongs to part of the original
                 training file which was set aside to serve as a test set)
               auto_tagged_sentence (list) : a sentence which has been tagged
                 automatically by a part-of-speech tagger
               separator (str) : the character which serves to separate
                 words from their part-of-speech tags (likely '_' or '/')
          """
          # split the hand-tagged sentence on whitespace, since the auto-tagged
          # sentence will already be split and we want them to match
          self.hand_tagged = hand_tagged_sentence.split()
          self.auto_tagged = auto_tagged_sentence
          self.sep = separator
  
      def strip_training_tags(self, hand_tagged_sentence):
          """Remove the part-of-speech tags from a test sentence."""
          return [w.split(self.sep, 1)[0] for w in self.hand_tagged]
  
subject: add SentencePair class and strip_training_tags method