In the first part of the series, we dealt extensively with text-preprocessing using NLTK and some manual processes; defining our model architecture; and training and evaluating a model, which we found good enough to be deployed based on the dataset we trained the model on.
Our next step is to reproduce the essential processes in production so that are able to synchronize expected outputs on new text inputs. We’ll start by converting the Notebook into scripts and modules in a different project environment, with necessary versions of libraries and frameworks installed.
Continue reading Building a Conversational Chatbot with NLTK and TensorFlow (Part 2)