There have been many efforts in Question Answer-ing in multiple languages. To evaluate each modelwith the actual and predicted answers we have cre-ated an Evaluation Tool1. The tool provides manyfeatures to the researcher to analyze the resultswith user-friendly UI/UX. We have used Flask2as a backend by synchronizing with the evaluat-ing script. Our tool can list the best and worst-performing samples for further analysis and candisplay collective EM and F1 score for the inputsamples.

[1] Hariom A. Pandya, Bhavik Ardeshna, Dr. Brijesh S. Bhatt Cascading Adaptors to Leverage English Data to Improve Performance ofQuestion Answering for Low-Resource Languages

BibTeX entry and citation info

@misc{pandya2021cascading,
      title={Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages}, 
      author={Hariom A. Pandya and Bhavik Ardeshna and Dr. Brijesh S. Bhatt},
      year={2021},
      eprint={2112.09866},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Get a copy of source code

Clone the SQuAD-Analytics (from the main branch) and cd into the directory.

git clone -b https://github.com/Bhavik-Ardeshna/SQuAD-Analytics.git
cd SQuAD-Analytic

Before Installation and Running Locally

Create uploads & images directory inside the SQuAD-Analytics directory

mkdir uploads
mkdir images
mkdir analysis

Installation and Running Locally

Step to Create Env

#if using python3
python3 -m venv env

python -m venv env

Step to run Flask Backend

#if using python3
pip3 install requirements.txt
python3 app.py

pip install requirements.txt
python app.py

Go to this URL

• localhost:5000