dipesg/nlp-question-answering-system

Question answering system with transformers

Question Answering System

This repository contains an implementation of the question-answering system. The main goal of the project is to learn working with 🤗 transformers architecture by replacing the default head with a custom head suitable for the task, and fine-tuning using custom data. In addition, the project tries to improve on the ability to recognise tricky (impossible) questions which are part of SQuAD 2.0 dataset. This project doesn't use QA task head coming with HuggingFace transformers but creates the head architecture from scratch. The same architecture is used to fine-tune 2 models, as described below.

The QA system is built using several sub-components:

• HuggingFace's DistilBERT transformer with custom head, fine-tuned on SQuAD v2.0, using only possible questions.
• HuggingFace's DistilBERT transformer with custom head, fine-tuned on SQuAD v2.0, using both - possible and non-possible questions.
• Inference component, combining the output of both models.

The logic behind training two models - the former is a conditional model, trained only on correct question/answers pairs, while the latter additionally includes tricky questions with answers that can't be found in the context. The idea is that combining the output of both models will improve the discrimination ability on impossible questions.

Web application

Explore the QA system using application hosted on Streamlit Sharing: https://share.streamlit.io/snexus/nlp-question-answering-system/main

Installation and running

• Clone the repository.

• Create and activate conda environment:

conda env create -f environment.yml
conda activate nlp-question-answering-system

• Download the trained models: TODO - store on publicly available service.

Training

Download the SQuAD 2.0 dataset for training

cd nlp-question-answering-system
./get_data.sh

Training locally

python train.py

Training on Google Colab

GC allows using GPU accelerated training by using GPU enabled runtime. To change runtime type, use Runtime-> Change runtime type.

To train with GC, use google_colab_train.ipynb from the notebooks folder.