IR2-project

Reproducibility Study of “InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval”

This project focuses on a reproducibility study of the InPars Toolkit, a tool designed for generating synthetic data to improve neural information retrieval (IR) systems. Our objective is to replicate and validate the methodology presented in the paper while improving on the future work proposed by the authors.

Installation

Step 1: Clone the Repository

git clone https://github.com/danilotpnta/IR2-project
cd IR2-project

Step 2: Create and Activate the Conda Environment

conda env create -f environment.yml
conda activate IR2-env

Running on Snellius

Follow Step 1 from the Installation section.

Step 2: Create and Activate Python Environment

WORK_DIR=$HOME/IR2-project
cd $WORK_DIR

source scripts/snellius_setup.sh
setup $PWD

Step 3: Install the Required Packages

# Install the required packages
pip install -e ".[all]"

# Additonally, if you want to use the vllm capabilities of the toolkit:
pip install vllm

# It might be faster to install using the requirements.txt file
pip install -r requirements.txt

Troubleshooting

When installing in Snellius you may want to install the packages using the --no-cache-dir flag. This will prevent the installation from using the cache and may solve some issues.

pip install --no-cache-dir -r requirements.txt

License

This project is licensed under the MIT License. See the LICENSE file for more details.