anchor.diff:  
   +h(2, "train-tagger-parser") Training the tagger and parser
   
  ++under-construction
  +
   p
       |  You can now train the model using a corpus for your language annotated
       |  with #[+a("http://universaldependencies.org/") Universal Dependencies].
  
positive.code:         assert len(tokens) == length
  
  +h(2, "vocabulary") Building the vocabulary
  
  p
      |  spaCy expects that common words will be cached in a
  
negative.code: 
  +h(3, "word-vectors") Training the word vectors
  
  +under-construction
  
  p
      |  #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
      |  algorithms let you train useful word similarity models from unlabelled
  