docs: def train_phrases(paths, out='data/bigram_model.phrases', tokenizer=word_tokenize, **kwargs):
      """
      Train a bigram phrase model on a list of files.
      """
      n = 0
      for path in paths:
          print('Counting lines for {0}...'.format(path))
          n += sum(1 for line in open(path, 'r'))
      print('Processing {0} lines...'.format(n))
  
      # Change to use less memory. Default is 40m.
      kwargs = {
          'max_vocab_size': 40000000,
          'threshold': 8.
      }.update(kwargs)
  
      print('Training bigrams...')
      bigram = Phrases(_phrase_doc_stream(paths, n, tokenizer=word_tokenize), **kwargs)
  
      print('Saving...')
      bigram.save(out)