title: doc2vec - How to infer vectors of documents faster?
body: <p>I have trained paragraph vectors for around 2300 paragraphs(between 2000-12000 words each) each with vector size of 300. Now, I need to infer paragraph vectors of around 100,000 sentences which I have considered as paragraphs(each sentence is around 10-30 words each corresponding to the earlier 2300 paragraphs already trained).</p>
  
  <p>So, am using </p>
  
  <p><code>model.infer_vector(sentence)</code></p>
  
  <p>But, the problem is it is taking too long, and it does not take any arguments such as "<code>workers</code>" .! Is there a way I can speed up the process by threading or some other way? I am using a machine with 8gb ram and when I checked the available cores using</p>
  
  <pre><code>cores = multiprocessing.cpu_count()
  </code></pre>
  
  <p>it comes out to be 8.</p>
  
  <p>I need this for answering multiple choice questions. Also, are there any other libraries/models such as <code>doc2vec</code> which can help in this task?</p>
  
  <p>Thanks in advance for your time.</p>