anchor.diff:      from spacy.vocab import Vocab
       from spacy.pipeline import EntityRecognizer
       from spacy.tokens import Doc
  +    from spacy.gold import GoldParse
   
       vocab = Vocab()
       entity = EntityRecognizer(vocab, entity_types=['PERSON', 'LOC'])
   
       doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
  -    entity.update(doc, ['O', 'O', 'B-PERSON', 'L-PERSON', 'O'])
  +    gold = GoldParse(doc, entities=['O', 'O', 'B-PERSON', 'L-PERSON', 'O'])
  +    entity.update(doc, gold)
   
       entity.model.end_training()
   
  
positive.code: 
  +code.
      from spacy.vocab import Vocab
      from spacy.pipeline import Tagger
      from spacy.tagger import P2_orth, P1_orth
      from spacy.tagger import P2_cluster, P1_cluster, W_orth, N1_orth, N2_orth
  
  
negative.code:     from spacy.vocab import Vocab
      from spacy.pipeline import DependencyParser
      from spacy.tokens import Doc
      from spacy.gold import GoldParse
  
      vocab = Vocab()
      parser = DependencyParser(vocab, labels=['nsubj', 'compound', 'dobj', 'punct'])
  
      doc = Doc(vocab, words=['Who', 'is', 'Shaka', 'Khan', '?'])
      gold = GoldParse(doc, [1,1,3,1,1], ['nsubj', 'ROOT', 'compound', 'dobj', 'punct'])
      parser.update(doc, gold)
  
      parser.model.end_training()
  
  