title: How to convert pretrained fastText vectors to gensim model
body: <p>How to convert pretrained fastText vectors to gensim model?
  I need predict_output_word method.</p>
  
  <p>import gensim
  from gensim.models import Word2Vec 
  from gensim.models.wrappers import FastText</p>
  
  <p>model_wiki = gensim.models.KeyedVectors.load_word2vec_format("wiki.ru.vec")
  model3 = Word2Vec(sentences=model_wiki)</p>
  
  <blockquote>
    <p>TypeError                                 Traceback (most recent call
    last)  in 
    ----> 1 model3 = Word2Vec(sentences=model_wiki)  # train a model from the corpus</p>
    
    <p>~/anaconda3/envs/pym/lib/python3.6/site-packages/gensim/models/word2vec.py
    in <strong>init</strong>(self, sentences, corpus_file, size, alpha, window,
    min_count, max_vocab_size, sample, seed, workers, min_alpha, sg, hs,
    negative, ns_exponent, cbow_mean, hashfxn, iter, null_word, trim_rule,
    sorted_vocab, batch_words, compute_loss, callbacks, max_final_vocab)
        765             callbacks=callbacks, batch_words=batch_words, trim_rule=trim_rule, sg=sg, alpha=alpha, window=window,
        766             seed=seed, hs=hs, negative=negative, cbow_mean=cbow_mean, min_alpha=min_alpha, compute_loss=compute_loss,
    --> 767             fast_version=FAST_VERSION)
        768 
        769     def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch,</p>
    
    <p>~/anaconda3/envs/pym/lib/python3.6/site-packages/gensim/models/base_any2vec.py
    in <strong>init</strong>(self, sentences, corpus_file, workers, vector_size,
    epochs, callbacks, batch_words, trim_rule, sg, alpha, window, seed,
    hs, negative, ns_exponent, cbow_mean, min_alpha, compute_loss,
    fast_version, **kwargs)
        757                 raise TypeError("You can't pass a generator as the sentences argument. Try an iterator.")
        758 
    --> 759             self.build_vocab(sentences=sentences, corpus_file=corpus_file, trim_rule=trim_rule)
        760             self.train(
        761                 sentences=sentences, corpus_file=corpus_file, total_examples=self.corpus_count,</p>
    
    <p>~/anaconda3/envs/pym/lib/python3.6/site-packages/gensim/models/base_any2vec.py
    in build_vocab(self, sentences, corpus_file, update, progress_per,
    keep_raw_vocab, trim_rule, **kwargs)
        934         """
        935         total_words, corpus_count = self.vocabulary.scan_vocab(
    --> 936             sentences=sentences, corpus_file=corpus_file, progress_per=progress_per, trim_rule=trim_rule)
        937         self.corpus_count = corpus_count
        938         self.corpus_total_words = total_words</p>
    
    <p>~/anaconda3/envs/pym/lib/python3.6/site-packages/gensim/models/word2vec.py
    in scan_vocab(self, sentences, corpus_file, progress_per, workers,
    trim_rule)    1569             sentences = LineSentence(corpus_file)<br>
    1570 
    -> 1571         total_words, corpus_count = self._scan_vocab(sentences, progress_per, trim_rule)    1572     1573 
    logger.info(</p>
    
    <p>~/anaconda3/envs/pym/lib/python3.6/site-packages/gensim/models/word2vec.py
    in _scan_vocab(self, sentences, progress_per, trim_rule)    1538<br>
    vocab = defaultdict(int)    1539         checked_string_types = 0
    -> 1540         for sentence_no, sentence in enumerate(sentences):    1541             if not checked_string_types:    1542<br>
    if isinstance(sentence, string_types):</p>
    
    <p>~/anaconda3/envs/pym/lib/python3.6/site-packages/gensim/models/keyedvectors.py
    in <strong>getitem</strong>(self, entities)
        337             return self.get_vector(entities)
        338 
    --> 339         return vstack([self.get_vector(entity) for entity in entities])
        340 
        341     def <strong>contains</strong>(self, entity):</p>
    
    <p>TypeError: 'int' object is not iterable</p>
  </blockquote>