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

Domain Application Industry Framework Training Data Input Data Format
Natural Language Processing (NLP) Question and Answer General TensorFlow SQuAD 1.1 Text



Component License Link
Model GitHub repository Apache 2.0 LICENSE
Fine-tuned Model Weights Apache 2.0 LICENSE
Pre-trained Model Weights Apache 2.0 LICENSE
Model Code (3rd party) Apache 2.0 LICENSE

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

A more elaborate tutorial on how to deploy this MAX model to production on IBM Cloud can be found here.

Example Usage

You can test or use this model

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": [
      "works for the EU",
      "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:

Resources and Contributions

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