NeuralQA Demo: Question Answering with BERT Models

NeuralQA provides a visual interface for end-to-end question answering (passage retrieval, query expansion, document reading, model explanation), on large datasets. Learn more on Github or from the blog series FF14 Automated Question Answering

This repository provides sample code on how to deploy NeuralQA on Cloudera Machine Learning (CML).

Launch the Application on CML

There are two ways to launch the NeuralQA prototype on CML:

1. From Prototype Catalog - Navigate to the Prototype Catalog on a CML workspace, select the "Neural Question Answering" tile, click "Launch as Project", click "Configure Project"
2. As ML Prototype - In a CML workspace, click "New Project", add a Project Name, select "ML Prototype" as the Initial Setup option, copy in the repo URL, click "Create Project", click "Configure Project"

Note: NeuralQA depends on several heavy libraries (Tensorflow, Pytorch, Transformers etc). A minimum of 6GB memory instance is recommended to run this template.