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  1. model = Doc2Vec(min_count=1, window=10, size=200, seed=SEED, sample=1e-4,
  2.                 alpha=0.025, negative=5, workers=24)
  3. model.build_vocab([x for x in tqdm(train_documents)])
  4. train_documents  = shuffle(train_documents)
  5. model.train(train_documents,total_examples=len(train_documents), epochs=30)
  7. def vector_for_learning(model, input_docs):
  8.     sents = input_docs
  9.     targets, feature_vectors = zip(*[(doc.tags[0], model.infer_vector(doc.words, steps=20)) for doc in sents])
  10.     return targets, feature_vectors
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