title: Word2Vec: Any way to train model fastly?
body: <p>I use <code>Gensim</code> <code>Word2Vec</code> to train word sets in my database.</p>
  
  <p>I have about 400,000 phrase(Each phrase is short. Total 700MB) in my <code>PostgreSQL</code> database.</p>
  
  <p>This is how I train these data using <code>Django ORM</code>: </p>
  
  <pre><code>post_vector_list = []
  for post in Post.objects.all():
      post_vector = my_tokenizer(post.category.name)
      post_vector.extend(my_tokenizer(post.title))
      post_vector.extend(my_tokenizer(post.contents))
      post_vector_list.append(post_vector)
  word2vec_model = gensim.models.Word2Vec(post_vector_list, window=10, min_count=2, size=300) 
  </code></pre>
  
  <p>But this job getting a lot of time and feels like not efficient.</p>
  
  <p>Especially, creating <code>post_vector_list</code> part took a lot of time and space..</p>
  
  <p>I want to improve speed of training but have no idea how to do.</p>
  
  <p>Want to get your advices. Thanks.</p>