new_contents: import argparse
  import logging
  import sys
  
  import mkl
  from gensim.models.word2vec import Word2Vec
  if __name__ == "__main__":
      logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
      mkl.set_num_threads(3)
  
      parser = argparse.ArgumentParser("Create topics from word2vec model")
      parser.add_argument("embedding_model", type=str)
      parser.add_argument("topic_model", type=str)
      args = parser.parse_args()
  
      assert "topic" in args.topic_model, "'%s' not a topic model" % args.topic_model
      assert "ssv" in args.topic_model, "need .ssv file for topic model"
      assert "embedding" in args.embedding_model, "'%s' not an embedding model" % args.embedding_model
  
      word2vec = Word2Vec.load_word2vec_format(args.model, binary=True)
  
      with open(args.topic_model, "r") as input:
          with open(args.embedding_model + ".ssv", "w") as output:
              for line in input:
                  split = line.split(" ")
                  best_topic_word = split[2] # first is topic, second is topic count, third is first word
                  most_similars = word2vec.most_similar([best_topic_word])
                  output.write("%s\n" % " ".join(most_similars))
  
subject: Add word2vec coherence script