code: def sent2examples(self, sent):
      
  
      # TODO(rmyeid): use expanders.
      words = [w if w in self.embeddings else TaggerBase.UNK for w in sent]
      ngrams = TaggerBase.ngrams(words, self.context, self.transfer)
      fvs = []
      for word, ngram in zip(sent, ngrams):
        fv = np.array([self.embeddings.get(w, self.embeddings.zero_vector()) for w in ngram]).flatten()
        if self.add_bias:
          fv = np.hstack((fv, np.array(1)))
        yield word, fv
docs: Convert ngrams into feature vectors.