      return label.partition(DELIMITER)[::2]
  
  
  def is_decorated(label):
      return label.find(DELIMITER) != -1
  
  
  def preprocess_training_data(gold_tuples, label_freq_cutoff=30):
      preprocessed = []
      freqs = {}
 
</s> remove         transitions = []
 </s> add         labels = {} </s> remove                         if label not in seen_entities:
                            seen_entities.add(label)
                            for move_str in ('B', 'I', 'L', 'U'):
                                actions[moves.index(move_str)].append(label)
 </s> add                         for action in (BEGIN, IN, LAST, UNIT):
                            actions[action][label] += 1
    return DELIMITER in label

def count_decorated_labels(gold_tuples):
    freqs = {}
    for raw_text, sents in gold_tuples:
        for (ids, words, tags, heads, labels, iob), ctnts in sents:
            proj_heads, deco_labels = projectivize(heads, labels)
            # set the label to ROOT for each root dependent
            deco_labels = ['ROOT' if head == i else deco_labels[i]
                           for i, head in enumerate(proj_heads)]
            # count label frequencies
            for label in deco_labels:
                if is_decorated(label):
                    freqs[label] = freqs.get(label, 0) + 1
    return freqs
      return label.partition(DELIMITER)[::2]
  
  
  def is_decorated(label):
    return DELIMITER in label

def count_decorated_labels(gold_tuples):
    freqs = {}
    for raw_text, sents in gold_tuples:
        for (ids, words, tags, heads, labels, iob), ctnts in sents:
            proj_heads, deco_labels = projectivize(heads, labels)
            # set the label to ROOT for each root dependent
            deco_labels = ['ROOT' if head == i else deco_labels[i]
                           for i, head in enumerate(proj_heads)]
            # count label frequencies
            for label in deco_labels:
                if is_decorated(label):
                    freqs[label] = freqs.get(label, 0) + 1
    return freqs
  
  
  def preprocess_training_data(gold_tuples, label_freq_cutoff=30):
      preprocessed = []
      freqs = {}
keep keep keep keep replace keep keep keep keep keep
 Revert "Merge branch 'develop' of https://github.com/explosion/spaCy into develop"

This reverts commit c9ba3d3c2dc7067cf8bd55f878cec45a8c6d73d4, reversing
changes made to 92c26a35d425d4e8ca1b805ea776ea10f5ded3df. 
spacy/syntax/nonproj.pyx