title: Is it possible to re-train a word2vec model (e.g. GoogleNews-vectors-negative300.bin) from a corpus of sentences in python?
body: <p>I am using pre-trained Google news dataset for getting word vectors by using Gensim library in python</p>
  
  <pre><code>model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
  </code></pre>
  
  <p>After loading the model I am converting training reviews sentence words into vectors</p>
  
  <pre><code>#reading all sentences from training file
  with open('restaurantSentences', 'r') as infile:
  x_train = infile.readlines()
  #cleaning sentences
  x_train = [review_to_wordlist(review,remove_stopwords=True) for review in x_train]
  train_vecs = np.concatenate([buildWordVector(z, n_dim) for z in x_train])
  </code></pre>
  
  <p>During word2Vec process i get a lot of errors for the words in my corpus, that are not in the model. Problem is how can i retrain already pre-trained model (e.g GoogleNews-vectors-negative300.bin'), in order to get word vectors for those missing words.</p>
  
  <p>Following is what I have tried:
  Trained a new model from training sentences that I had</p>
  
  <pre><code># Set values for various parameters
  num_features = 300    # Word vector dimensionality                      
  min_word_count = 10   # Minimum word count                        
  num_workers = 4       # Number of threads to run in parallel
  context = 10          # Context window    size                                                                                    
  downsampling = 1e-3   # Downsample setting for frequent words
  
  sentences = gensim.models.word2vec.LineSentence("restaurantSentences")
  # Initialize and train the model (this will take some time)
  print "Training model..."
  model = gensim.models.Word2Vec(sentences, workers=num_workers,size=num_features, min_count = min_word_count, 
                        window = context, sample = downsampling)
  
  
  model.build_vocab(sentences)
  model.train(sentences)
  model.n_similarity(["food"], ["rice"])
  </code></pre>
  
  <p>It worked! but the problem is that I have a really small dataset and less resources to train a large model.</p>
  
  <p>Second way that I am looking at is to extend the already trained model such as GoogleNews-vectors-negative300.bin.</p>
  
  <pre><code>model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
  sentences = gensim.models.word2vec.LineSentence("restaurantSentences")
  model.train(sentences)
  </code></pre>
  
  <p>Is it possible and is that a good way to use, please help me out</p>