title: After training word embedding with gensim's fasttext's wrapper, how to embed new sentences?
body: <p>After reading the tutorial at gensim's <a href="https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/FastText_Tutorial.ipynb" rel="nofollow noreferrer">docs</a>, I do not understand what is the correct way of generating new embeddings from a trained model. So far I have trained gensim's fast text embeddings like this:</p>
  
  <pre><code>from gensim.models.fasttext import FastText as FT_gensim
  
  model_gensim = FT_gensim(size=100)
  
  # build the vocabulary
  model_gensim.build_vocab(corpus_file=corpus_file)
  
  # train the model
  model_gensim.train(
      corpus_file=corpus_file, epochs=model_gensim.epochs,
      total_examples=model_gensim.corpus_count, total_words=model_gensim.corpus_total_words
  )
  </code></pre>
  
  <p>Then, let's say I want to get the embeddings vectors associated with this sentences:</p>
  
  <pre><code>sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()
  sentence_president = 'The president greets the press in Chicago'.lower().split()
  </code></pre>
  
  <p>How can I get them with <code>model_gensim</code> that I trained previously?</p>