multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks. We support various data formats for majority of NLI tasks and multiple transformer-based encoders (eg. BERT, Distil-BERT, ALBERT, RoBERTa, XLNET etc.)


What is multi_task_NLP about?

Any conversational AI system involves building multiple components to perform various tasks and a pipeline to stitch all components together. Provided the recent effectiveness of transformer-based models in NLP, it’s very common to build a transformer-based model to solve your use case. But having multiple such models running together for a conversational AI system can lead to expensive resource consumption, increased latencies for predictions and make the system difficult to manage. This poses a real challenge for anyone who wants to build a conversational AI system in a simplistic way.

multi_task_NLP gives you the capability to define multiple tasks together and train a single model which simultaneously learns on all defined tasks. This means one can perform multiple tasks with latency and resource consumption equivalent to a single task.


To use multi-task-NLP, you can clone the repository into the desired location on your system with the following terminal command.

$ cd /desired/location/
$ git clone
$ cd multi-task-NLP
$ pip install -r requirements.txt

NOTE:- The library is built and tested using Python 3.7.3. It is recommended to install the requirements in a virtual environment.

Quickstart Guide

A quick guide to show how a single model can be trained for multiple NLI tasks in just 3 simple steps and with no requirement to code!!

Examples Guide

We provide exemplar notebooks to demonstrate some conversational AI tasks which can be perfomed using our library. You can follow along the notebooks to understand and train a multi-task model for the tasks.