  Reproducing results in publications by distributing publicly available source
code is becoming ever more popular. Given the difficulty of reproducing machine
learning (ML) experiments, there have been significant efforts in reducing the
variance of these results. As in any science, the ability to consistently
reproduce results effectively strengthens the underlying hypothesis of the
work, and thus, should be regarded as important as the novel aspect of the
research itself. The contribution of this work is a framework that is able to
reproduce consistent results and provides a means of easily creating, training,
and evaluating natural language processing (NLP) deep learning (DL) models.
