ALBERT API for Question Answering

An exploration in using Google's ALBERT method for training NLP models to build a question answering service.

The goal is to build an API that -- given some document text and a question -- can predict how well an algorithm can answer that question from a document.

This project leverages Huggingface's Transformers implementation of the ALBERT model.

Pre-Requisites

This project uses Docker to handle Python environments, networking, and the like. Please ensure you have it installed on your machine.

Setup

The project Makefile handles fetching dependencies, building Docker images, and running the project.

Some helpful commands:

• make run: runs the Albert API service
• make run-repl: opens a Python REPL in the container with all the library dependencies installed and available
• make run-sh: opens a shell in the container with all the library dependencies installed and available
• make build: builds the Docker image
• make transformers: checks out the Huggingface transformers project

Demo App

This project comes with a simple HTML UI to try out ALBERT. Open your Terminal application to the folder where you downloaded htis code and run make run.

Wait for the Running on http://0.0.0.0:5000/ message in your console and then open your browser to http://0.0.0.0:5000.

Note, the first document you analyze may take time to download the required models.

Press CTRL+C to quit.

Command-Line Usage

This project comes with a script to run examples. First, make sure you have read and completed the steps in the Pre-Requisites section.

NOTE: this script is written in bash, so you need either Linux or Mac to run it. You could try Windows Subsystem for Linux but I can't test on that.

Open a terminal and run make run to boot up a server. You should see some logs as it boots up. Wait until you see Running on http://0.0.0.0:5000/ (Press CTRL+C to quit).

Leave this running. In a separate terminal, run the following:

$ echo 'Jen Henson was a nice muppet.' > example.txt
$ ./run-api.sh load example.txt
00dLViX04XwGhw== # Note, this is an ID the API will return

This will return an ID for that document. Copy that into your clipboard. Then ask a question to the API and ALBERT will try to answer it from its contents.

$ ./run-api.sh ask 00dLViX04XwGhw== "Who was Jim Henson?"
▁henson ▁was ▁a ▁nice ▁muppet

NOTE: On your first run, the transformers library will download pre-trained models. You'll see this progress in the terminal window running the server.

ALBERT is a model that works on probabilities and likelihoods. You may get different results when you shut down and start the server back up again.

Contributing

This is where you come in. This should be more reliable. I don't have deep experience with Machine Leanring, NLP, or even Python for that matter.

I'm considering a few things to try:

• Find some examples of search queries, feature snippets, and the web page contents they come from as training data to finetune the model
• Continue refining the Python setup; I have no idea if I did it right and the documentation is a bit scarce
• Explore returning a "probability" or "likelihood" rather than a binary "yes/no" on an "answerability" score