Question Answering

Overview

Given a body of text (context) about a subject and questions about that subject, the model will answer questions based on the given context.

The model is based on the BERT model.

Model Metadata

References

• J. Devlin, M. Chang, K. Lee, K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, arXiv, 2018.
• Google BERT
• SQuAD Dataset and version 1.1 on the Google BERT repo

Licenses

Options available for deploying this model

This model can be deployed using the following mechanisms:

• Deploy from Dockerhub:

  docker run -it -p 5000:5000 codait/max-question-answering
  

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• Deploy on Kubernetes:

  kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Question-Answering/master/max-question-answering.yaml
  

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• Locally: follow the instructions in the model README on GitHub

Example Usage

You can test or use this model

• using cURL
• in a notebook

Test the model using cURL

Once deployed, you can test the model from the command line. For example if running locally:

curl -X POST "http://localhost:5000/model/predict" -H "accept: application/json" -H "Content-Type: application/json" -d "{\"paragraphs\": [{ \"context\": \"John lives in Brussels and works for the EU\", \"questions\": [\"Where does John Live?\",\"What does John do?\",\"What is his name?\" ]},{ \"context\": \"Jane lives in Paris and works for the UN\", \"questions\": [\"Where does Jane Live?\",\"What does Jane do?\" ]}]}"

{
  "status": "ok",
  "predictions": [
    [
      "Brussels",
      "works for the EU",
      "John"
    ],
    [
      "Paris",
      "works for the UN"
    ]
  ]
}

Test the model in a notebook

The demo notebook walks through how to use the model to answer questions on a given corpus of text. By default, the notebook uses the hosted demo instance, but you can use a locally running instance.

Run the following command from the model repo base folder, in a new terminal window:

jupyter notebook

This will start the notebook server. You can launch the demo notebook by clicking on samples/demo.ipynb.

Options available for training this model

This model can be trained using the following mechanisms:

• Train on IBM Cloud – Watson Machine Learning: follow the instructions in the model training README on GitHub.