new_contents: from gensim.models import Word2Vec
  from gensim.models.word2vec import PathLineSentences
  from sense2vec.vectors import VectorMap
  import plac
  
  
  @plac.annotations(
      in_dir=("Location of input directory", "positional", None, str),
      out_file=("Location of output file", "positional", None, str),
      n_workers=("Number of workers", "option", "n", int),
      size=("Dimension of the word vectors", "option", "d", int),
      window=("Context window size", "option", "w", int),
      min_count=("Min count", "option", "m", int),
      negative=("Number of negative samples", "option", "g", int),
      nr_iter=("Number of iterations", "option", "i", int),)
  def train(in_dir, out_file, negative=5, n_workers=4, window=5, size=128,
            min_count=10, nr_iter=2):
      w2v_model = Word2Vec(size=size, window=window, min_count=min_count,
                           workers=workers, sample=1e-5, negative=negative,
                          iter=epochs)
      sentences = PathLineSentences(in_dir)
      print("Building the vocabulary...")
      w2v_model.build_vocab(sentences)
      print("Training the model...")
      w2v_model.train(sentences, total_examples=w2v_model.corpus_count,
                      epochs=w2v_model.iter)
      print("Creating the sense2vec model...")
      vector_map = VectorMap(size)
      for string in w2v_model.wv.vocab:
          vocab = w2v_model.wv.vocab[string]
          freq, idx = vocab.count, vocab.index
          if freq < min_count:
              continue
          vector = w2v_model.wv.vectors[idx]
          vector_map.borrow(string, freq, vector)
      print("Saving the model...")
      vector_map.save(out_file)
      print("Saved model to file: ", out_file)
  
  
  if __name__ == '__main__':
      plac.call(main)
  
subject: add training script