title: What are doc2vec training iterations?
body: <p>I am new to doc2vec. I was initially trying to understand doc2vec and mentioned below is my code that uses Gensim. As I want I get a trained model and document vectors for the two documents.</p>
  
  <p>However, I would like to know the benefits of retraining the model in several epoches and how to do it in Gensim? Can we do it using <code>iter</code> or <code>alpha</code> parameter or do we have to train it in a seperate <code>for loop</code>? Please let me know how I should change the following code to train the model for 20 epoches.</p>
  
  <p>Also, I am interested in knowing is the multiple training iterations are needed for word2vec model as well.</p>
  
  <pre><code># Import libraries
  from gensim.models import doc2vec
  from collections import namedtuple
  
  # Load data
  doc1 = ["This is a sentence", "This is another sentence"]
  
  # Transform data
  docs = []
  analyzedDocument = namedtuple('AnalyzedDocument', 'words tags')
  for i, text in enumerate(doc1):
      words = text.lower().split()
      tags = [i]
      docs.append(analyzedDocument(words, tags))
  
  # Train model
  model = doc2vec.Doc2Vec(docs, size = 100, window = 300, min_count = 1, workers = 4)
  
  # Get the vectors
  model.docvecs[0]
  model.docvecs[1]
  </code></pre>