Computing Perplexity

[waelbenamara]

Is there anyway to return perplexity with respect to the number of iterations? In case we want to optimise the number of iteration and avoid getting into burn-in periods in future executions.
A way I found to do that, is to add a list attribute that is iteratively filled with the perplexity corresponding to that iteration.
Perplexity is computed as exp(-(modelLogLikelihood() / totalTokens)))
Any chance I can submit a pull request?

[mimno]

Thanks for looking into this! I'm not sure I understand. Is the idea to stop training when model log likelihood stops dropping? Burn-in usually refers to the early iterations, while log likelihood is still improving.