title: Adjust Google's Word2Vec loaded with Gensim to your vocabulary and then create the embedding vector
body: <p>I was wondering how can I limited the Google's Word2Vec to my vocabulary.
  Google's Word2 vec link:<a href="https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing" rel="nofollow noreferrer">https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing</a></p>
  
  <p>This is what I have:</p>
  
  <pre><code>import gensim
  
  # Load Google's pre-trained Word2Vec model.
  model = gensim.models.Word2Vec.load_word2vec_format('./model/GoogleNews-vectors-negative300.bin', binary=True)
  
  embedding_matrix = np.zeros((len(my_vocabulary), 300))
  </code></pre>
  
  <p>where my vocabulary is a list of unique words in my corpus. 
  How can I feel the embedding matrix only for words in my_vocabulary?
  In addition, I would like to have the flexibility that if my word does not exist in the Google's word2vec to be filled with zeros.</p>
  
  <p>Thanks</p>