title: Ensure the gensim generate the same Word2Vec model for different runs on the same data
body: <p>In <a href="https://stackoverflow.com/questions/15067734/lda-model-generates-different-topics-everytime-i-train-on-the-same-corpus">LDA model generates different topics everytime i train on the same corpus</a> , by setting the <code>np.random.seed(0)</code>, the LDA model will always be initialized and trained in exactly the same way. </p>
  
  <p><strong>Is it the same for the Word2Vec models from <code>gensim</code>? By setting the random seed to a constant, would the different run on the same dataset produce the same model?</strong></p>
  
  <p>But strangely, it's already giving me the same vector at different instances. </p>
  
  <pre><code>&gt;&gt;&gt; from nltk.corpus import brown
  &gt;&gt;&gt; from gensim.models import Word2Vec
  &gt;&gt;&gt; sentences = brown.sents()[:100]
  &gt;&gt;&gt; model = Word2Vec(sentences, size=10, window=5, min_count=5, workers=4)
  &gt;&gt;&gt; model[word0]
  array([ 0.04985042,  0.02882229, -0.03625415, -0.03165979,  0.06049283,
          0.01207791,  0.04722737,  0.01984878, -0.03026265,  0.04485954], dtype=float32)
  &gt;&gt;&gt; model = Word2Vec(sentences, size=10, window=5, min_count=5, workers=4)
  &gt;&gt;&gt; model[word0]
  array([ 0.04985042,  0.02882229, -0.03625415, -0.03165979,  0.06049283,
          0.01207791,  0.04722737,  0.01984878, -0.03026265,  0.04485954], dtype=float32)
  &gt;&gt;&gt; model = Word2Vec(sentences, size=20, window=5, min_count=5, workers=4)
  &gt;&gt;&gt; model[word0]
  array([ 0.02596745,  0.01475067, -0.01839622, -0.01587902,  0.03079717,
          0.00586761,  0.02367715,  0.00930568, -0.01521437,  0.02213679,
          0.01043982, -0.00625582,  0.00173071, -0.00235749,  0.01309298,
          0.00710233, -0.02270884, -0.01477827,  0.01166443,  0.00283862], dtype=float32)
  &gt;&gt;&gt; model = Word2Vec(sentences, size=20, window=5, min_count=5, workers=4)
  &gt;&gt;&gt; model[word0]
  array([ 0.02596745,  0.01475067, -0.01839622, -0.01587902,  0.03079717,
          0.00586761,  0.02367715,  0.00930568, -0.01521437,  0.02213679,
          0.01043982, -0.00625582,  0.00173071, -0.00235749,  0.01309298,
          0.00710233, -0.02270884, -0.01477827,  0.01166443,  0.00283862], dtype=float32)
  &gt;&gt;&gt; exit()
  alvas@ubi:~$ python
  Python 2.7.11 (default, Dec 15 2015, 16:46:19) 
  [GCC 4.8.4] on linux2
  Type "help", "copyright", "credits" or "license" for more information.
  &gt;&gt;&gt; from nltk.corpus import brown
  &gt;&gt;&gt; from gensim.models import Word2Vec
  &gt;&gt;&gt; sentences = brown.sents()[:100]
  &gt;&gt;&gt; model = Word2Vec(sentences, size=10, window=5, min_count=5, workers=4)
  &gt;&gt;&gt; word0 = sentences[0][0]
  &gt;&gt;&gt; model[word0]
  array([ 0.04985042,  0.02882229, -0.03625415, -0.03165979,  0.06049283,
          0.01207791,  0.04722737,  0.01984878, -0.03026265,  0.04485954], dtype=float32)
  &gt;&gt;&gt; model = Word2Vec(sentences, size=20, window=5, min_count=5, workers=4)
  &gt;&gt;&gt; model[word0]
  array([ 0.02596745,  0.01475067, -0.01839622, -0.01587902,  0.03079717,
          0.00586761,  0.02367715,  0.00930568, -0.01521437,  0.02213679,
          0.01043982, -0.00625582,  0.00173071, -0.00235749,  0.01309298,
          0.00710233, -0.02270884, -0.01477827,  0.01166443,  0.00283862], dtype=float32)
  </code></pre>
  
  <p><strong>Is it true then that the default random seed is fixed?</strong> If so, what is the default random seed number? Or is it because I'm testing on a small dataset? </p>
  
  <p>If it's true that the the random seed is fixed and different runs on the same data returns the same vectors, a link to a canonical code or documentation would be much appreciated.  </p>