title: How to access document details from Doc2Vec similarity scores in gensim model?
body: <p>I have been given a doc2vec model using gensim which was trained on 20 Million documents. The 20 Million documents it was trained are also given to me but I have no idea how or which order the documents were trained in from the folder. I am supposed to use the test data to find the top 10 match from the training set. The code I use is - </p>
  
  <pre><code>model = gensim.models.doc2vec.Doc2Vec.load("doc2vec_sample.model")
  
  test_docs=["This is the test set I want to test on."]
  
  def read_corpus(documents, tokens_only=False):
      count=0
      count=count+1
      for line in documents:
          if tokens_only:
              yield gensim.utils.simple_preprocess(line)
          else:
              # For training data, add tags
              yield gensim.models.doc2vec.TaggedDocument(gensim.utils.simple_preprocess(line), [count])
  
  
  test_corpus = list(read_corpus(test_docs, tokens_only=True))
  
  doc_id=0
  
  inferred_vector = model.infer_vector(test_corpus[doc_id])
  maxx=10
  sims = model.docvecs.most_similar([inferred_vector], topn=maxx)
  
  for match in sims:
      print match
  </code></pre>
  
  <p>`
  The output I get is -</p>
  
  <pre><code>(1913, 0.4589531719684601)
  (3250, 0.4300411343574524)
  (1741, 0.42669129371643066)
  (1, 0.4023148715496063)
  (1740, 0.3929900527000427)
  (1509, 0.39229822158813477)
  (3189, 0.387174129486084)
  (3145, 0.3842133581638336)
  (1707, 0.3813004493713379)
  (3200, 0.3754497170448303)
  </code></pre>
  
  <p>How do I get to know which document does document id "1913" refer to? How can I access the documents of the trained data set from these 10 job ids? </p>