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Question Answering Notebook

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Description

End to End workflow for question answering starting with training in TLT and deployment using Riva.

Publisher

NVIDIA

Use Case

Other

Framework

TransferLearningToolkit

Latest Version

v1.0

Modified

August 24, 2021

Compressed Size

46.21 KB

Question Answering

The Question Answering task in NLP pertains to building a model which can answer questions posed in natural language. Many datasets (including SQuAD, the dataset we use in this notebook) pose this as a reading comprehension task i.e. given a question and a context, the goal is to predict the span within the context with a start and end position which indicates the answer to the question. For every word in the training dataset we predict:

  • likelihood this word is the start of the span
  • likelihood this word is the end of the span

The best place to get started with TLT - Question Answering would be the TLT - Question Answering jupyter notebooks. This resource has two notebooks included.

  1. Training: Sample workflow for training a question answering model and export the model to a .riva file
  2. Deployment: Sample workflow to consume the .riva file and deploy it to Riva.

If you are a seasoned Conversation AI developer we recommend installing TLT and referring to the TLT documentation for usage information.

Pre-Requisites

Please make sure to install the following before proceeding further:

  • python 3.6.9
  • docker-ce > 19.03.5
  • docker-API 1.40
  • nvidia-container-toolkit > 1.3.0-1
  • nvidia-container-runtime > 3.4.0-1
  • nvidia-docker2 > 2.5.0-1
  • nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TLT inside a virtual environment. The steps to do the same are as follows.

virtualenv -p python3 
source /bin/activate
pip install jupyter notebook # If you need to run the notebooks

TLT is python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex
pip install nvidia-tlt

To download the jupyter notebook please:

  1. Download the samples using the ngc cli with the following command
ngc registry resource download-version "nvidia/tlt-riva/questionanswering_notebook:v1.0"
  1. Instantiate the jupyter notebook server
jupyter notebook --ip 0.0.0.0 --allow-root --port 8888

License

By downloading and using the models and resources packaged with TLT Conversational AI, you would be accepting the terms of the Riva license