title: Load fasttext model faster by excluding certain vocabulary
body: <p>Loading the pretrained fasttext wordvectors released by Facebook Research take a very long time on a local machine, which I do like this:</p>
  
  <pre><code>model =  fs.load_word2vec_format('wiki.en.vec') 
  print(model['test']) # get the vector of the word 'test'
  </code></pre>
  
  <p>I am seeking to reduce the load time by <strong>removing wordvectors for words that don't appear in my dataset</strong>. I.e. I want to reduce the pretrained vector model to the words that comprise the dataset I need to analyse, which is a subset of the pre-trained model.</p>
  
  <p>I was about to try and build a new model by extracting the wordvectors I need and saving to a new model, but the type would change from <em>FastTextKeyedVectors</em> to <em>FastText</em>:</p>
  
  <pre><code>#model2 = gensim.models.Word2Vec(iter=1)
  #model2 = FastText()
  for word in words:
      if (word in model):
         model2[] = model[word]
  </code></pre>
  
  <p>How can I reduce my load time ? Do my approaches make sense or am I on the wrong path ?</p>