VBF Transformer Project

This project implements a Transformer-based model for VBF Di-Tau analysis.

Installation

1. Create the Conda Environment

First, create the conda environment using the provided requirements.yaml file:

conda env create -f requirements.yaml

Then, activate the environment:

conda activate vbf_ditau

2. Install the VBFTransformer Package

Install the project as a Python package in editable mode. This allows you to make changes to the source code and have them immediately reflected without reinstalling.

pip install -e .

Usage

The main script VBFTransformer.py is controlled via the command line and configured with Hydra. You can run the project in three different modes: train, predict, and performance.

Training the Model

To train the model, run:

python VBFTransformer.py --config-name config general.mode=train

Generating Predictions

To generate predictions with a trained model, run:

python VBFTransformer.py --config-name config general.mode=predict

Evaluating Performance

To evaluate the model's performance (e.g., generate ROC curves and confusion matrices), run:

python VBFTransformer.py --config-name config general.mode=performance

You can customize the behavior by modifying the configs/config.yaml file or by overriding parameters from the command line. For example:

python VBFTransformer.py general.mode=train model.learning_rate=0.002