anchor.diff:      assert doc[1].orth == doc_array[1, 0]
       assert doc[0].like_url == doc_array[0, 1]
       assert list(doc_array[:, 1]) == [t.like_url for t in doc]
  -
  -+h(2, "examples-word-vectors") Word vectors
  -
  -+code.
  -    doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
  -
  -    apples = doc[0]
  -    oranges = doc[2]
  -    boots = doc[6]
  -    hippos = doc[8]
  -
  -    assert apples.similarity(oranges) > boots.similarity(hippos)
  -
  -+h(2, "examples-pos-tags") Part-of-speech tags
  -
  -+code.
  -    from spacy.parts_of_speech import ADV
  -
  -    def is_adverb(token):
  -        return token.pos == spacy.parts_of_speech.ADV
  -
  -    # These are data-specific, so no constants are provided. You have to look
  -    # up the IDs from the StringStore.
  -    NNS = nlp.vocab.strings['NNS']
  -    NNPS = nlp.vocab.strings['NNPS']
  -    def is_plural_noun(token):
  -        return token.tag == NNS or token.tag == NNPS
  -
  -    def print_coarse_pos(token):
  -        print(token.pos_)
  -
  -    def print_fine_pos(token):
  -        print(token.tag_)
  -
  -+h(2, "examples-dependencies") Syntactic dependencies
  -
  -+code.
  -    def dependency_labels_to_root(token):
  -        '''Walk up the syntactic tree, collecting the arc labels.'''
  -        dep_labels = []
  -        while token.head is not token:
  -            dep_labels.append(token.dep)
  -            token = token.head
  -        return dep_labels
  -
  -+h(2, "examples-entities") Named entities
  -
  -+code.
  -    def iter_products(docs):
  -        for doc in docs:
  -            for ent in doc.ents:
  -                if ent.label_ == 'PRODUCT':
  -                    yield ent
  -
  -    def word_is_in_entity(word):
  -        return word.ent_type != 0
  -
  -    def count_parent_verb_by_person(docs):
  -        counts = defaultdict(lambda: defaultdict(int))
  -        for doc in docs:
  -            for ent in doc.ents:
  -                if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:
  -                    counts[ent.orth_][ent.root.head.lemma_] += 1
  -        return counts
   
   +h(2, "examples-inline") Calculate inline mark-up on original string
   
  
positive.code:         if i == 100:
              break
  
  +h(2, "examples-tokens-sentences") Get tokens and sentences
  
  +code.
      token = doc[0]
      sentence = next(doc.sents)
      assert token is sentence[0]
      assert sentence.text == 'Hello, world.'
  
  +h(2, "examples-integer-ids") Use integer IDs for any string
  
  +code.
      hello_id = nlp.vocab.strings['Hello']
      hello_str = nlp.vocab.strings[hello_id]
  
      assert token.orth  == hello_id  == 3125
      assert token.orth_ == hello_str == 'Hello'
  
  +h(2, "examples-string-views-flags") Get and set string views and flags
  
  +code.
      assert token.shape_ == 'Xxxxx'
      for lexeme in nlp.vocab:
          if lexeme.is_alpha:
              lexeme.shape_ = 'W'
          elif lexeme.is_digit:
              lexeme.shape_ = 'D'
          elif lexeme.is_punct:
              lexeme.shape_ = 'P'
          else:
              lexeme.shape_ = 'M'
      assert token.shape_ == 'W'
  
  +h(2, "examples-numpy-arrays") Export to numpy arrays
  
  
negative.code:         string = string.replace('\n', '')
          string = string.replace('\t', '    ')
          return string
  