Skip to content
Answer questions on a given corpus of text.
Python Shell Dockerfile
Branch: master
Clone or download
Latest commit 6347564 Feb 6, 2020
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
api Issue fixes (#13) Sep 19, 2019
core Issue fixes. (#9) Sep 13, 2019
docs Initial commit Sep 11, 2019
samples
tests Enables training on WML. (#15) Oct 19, 2019
training Remove trailing whitespace. (#31) Jan 17, 2020
.dockerignore
.gitignore Update .gitignore (#19) Oct 22, 2019
.travis.yml Initial commit Sep 11, 2019
Dockerfile
LICENSE
README.md Remove trailing whitespace. (#31) Jan 17, 2020
app.py Initial commit Sep 11, 2019
config.py
max-question-answering.yaml
requirements-test.txt Initial commit Sep 11, 2019
requirements.txt Bump tensorflow from 1.15.0 to 1.15.2 (#32) Feb 6, 2020
sha512sums.txt Issue fixes. (#9) Sep 13, 2019

README.md

Build Status Website Status

IBM Developer Model Asset Exchange: Question Answering Model

This repository contains code to instantiate and deploy a Question Answering model. 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. The model files are hosted on IBM Cloud Object Storage. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Developer Model Asset Exchange and the public API is powered by IBM Cloud.

Model Metadata

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

Note: the SQuAD 1.1 files are no longer on the dataset website but can be found on the Google BERT repo

Benchmark

The predictive performance of the model can be characterized by the benchmark table below.

Note: The performance of a model is not the only significant metric. The level of bias and fairness incorporated in the model are also of high importance. Learn more by reading up on the AI Fairness 360 open source toolkit.

On datasets where the answers given are designed to not be exact matches to a span of text from the given context (MS MARCO), the model does not perform as well, since SQuAD 1.1 is a span-matching task.

SQuAD 1.1 TriviaQA 1.0 for RC MS MARCO
f1 Score 88.7 60.9 40.7
Exact Match 81.5 53.8 9.4

References

Licenses

Component License Link
This 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
Test samples CC BY-SA 4.0 samples README

Pre-requisites:

  • docker: The Docker command-line interface. Follow the installation instructions for your system.
  • The minimum recommended resources for this model is 2 GiB Memory and 4 CPUs.

Deployment Options

Deploy from Docker Hub

To run the docker image, which automatically starts the model serving API, run:

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

This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.

Deploy on Red Hat OpenShift

You can deploy the model-serving microservice on Red Hat OpenShift by following the instructions for the OpenShift web console or the OpenShift Container Platform CLI in this tutorial, specifying codait/max-question-answering as the image name.

Deploy on Kubernetes

You can also deploy the model on Kubernetes using the latest docker image on Docker Hub.

On your Kubernetes cluster, run the following commands:

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

The model will be available internally at port 5000, but can also be accessed externally through the NodePort.

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

Run Locally

  1. Build the Model
  2. Deploy the Model
  3. Use the Model
  4. Run the Notebook
  5. Development
  6. Cleanup

1. Build the Model

Clone this repository locally. In a terminal, run the following command:

$ git clone https://github.com/IBM/MAX-Question-Answering.git

Change directory into the repository base folder:

$ cd MAX-Question-Answering

To build the docker image locally, run:

$ docker build -t max-question-answering .

All required model assets will be downloaded during the build process. Note that currently this docker image is CPU only (we will add support for GPU images later).

2. Deploy the Model

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-question-answering

3. Use the Model

The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.

Use the model/predict endpoint to upload a test json file (you can use/alter the files from the samples folder) and get answers to the questions from the API.

Sample input:

{
  "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?"
      ]
    }
  ]
}

Example of getting answers from the API

You can also test it on the command line, for example:

$ 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?\" ]}]}"

You should see a JSON response like that below:

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

4. Run the Notebook

The demo notebook walks through how to use the model to run inference on a text file or on text in-memory. By default, the notebook uses the hosted demo instance, but you can use a locally running instance as well. Note the demo requires jupyter, pprint, json and requests.

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.

5. Development

To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the docker image (see step 1).

6. Cleanup

To stop the Docker container, type CTRL + C in your terminal.

Train this Model on Watson Machine Learning

This model supports training from scratch on a custom dataset. Please follow the steps listed under the training README to retrain the model on Watson Machine Learning, a deep learning as a service offering of IBM Cloud.

Resources and Contributions

If you are interested in contributing to the Model Asset Exchange project or have any queries, please follow the instructions here.

You can’t perform that action at this time.