code: def from_word2vec(fname, fvocab=None, binary=False):
      
      vocabulary = None
      if fvocab is not None:
        logger.info("loading word counts from %s" % (fvocab))
        vocabulary = Embedding.from_word2vec_vocab(fvocab)
  
      logger.info("loading projection weights from %s" % (fname))
      if binary:
        words, vectors = Embedding._from_word2vec_binary(fname)
      else:
        words, vectors = Embedding._from_word2vec_text(fname)
  
      if not vocabulary:
        vocabulary = OrderedVocabulary(words=words)
  
      return Embedding(vocabulary=vocabulary, vectors=vectors)
docs: Load the input-hidden weight matrix from the original C word2vec-tool format.
  
      Note that the information stored in the file is incomplete (the binary tree is missing),
      so while you can query for word similarity etc., you cannot continue training
      with a model loaded this way.
  
      `binary` is a boolean indicating whether the data is in binary word2vec format.
      Word counts are read from `fvocab` filename, if set (this is the file generated
      by `-save-vocab` flag of the original C tool).