title: Gensim resume training starting training from scratch
body: <p>I am attempting to train an encoding for a text using gensim. I run the text over 5000 iterations (Document is ~250,000 words long, and Gensim is training over ~7500 frequent words), and the summed training loss for each iteration reduces from ~800,000 to 4,000 in that time. That said, when plotted, it is clear that training ended early and that further training would reduce the loss. However, when I load back in the saved model and run training:</p>
  
  <pre><code>model = Word2Vec.load("encodings")
  model.train(lines, total_examples=model.corpus_count, epochs=model.iter, compute_loss=True, callbacks=[logLoss, saveModel])
  </code></pre>
  
  <p>Training appears to start over from scratch with the initial training error ~800,000 and reduces back down to 4,000. I am saving the model with <code>model.save("encodings")</code>, and the saved model appears to be at least partially trained as word vector similarities seem somewhat reasonable. I want to further train my encoding, but this isn't working. (Note, initially training my encoding for more time ie. <code>iter=10000</code> doesn't improve my loss (it starts at 800,000 and ends at 4,000). Reducing the initial alpha or min_alpha over this extended period does not help either. Initial training is run with:</p>
  
  <pre><code>model = Word2Vec(lines, min_count=2, size=300, workers=8, sg=1, iter=5000, compute_loss=True, callbacks=[logLoss, saveModel])
  </code></pre>