This is a template repository for creating an experiment environment in Python. It intends to speed up the research process - reducing the repository structure design - and to have it clean and concise through multiple experiments.
Inspired by the cookiecutter folder structure.
Instructions:
- Search for all TODOs in the project and add the appropriate values
- Rename this README title and description
Before starting the project make sure these requirements are available:
- python. For setting up the environment and Python dependencies (version 3.8 or higher).
- git. For versioning your code.
First, create a virtual environment where all the modules will be stored.
Using the venv command, run the following commands:
# create a new virtual environment
python -m venv venv
# activate the environment (UNIX)
source ./venv/bin/activate
# activate the environment (WINDOWS)
./venv/Scripts/activate
# deactivate the environment (UNIX & WINDOWS)
deactivateCheck the requirements.txt file. If you have any additional requirements, add them here.
To install the requirements run:
pip install -e .TODO: Provide information about the data used in the experiments
- Where is the data found
- How is the data structured
To run the experiments, run the following commands:
TODO: Provide scripts for the experimentsThe results folder contains the experiment
TODO: Provide a list/table of experiment results
This project produced the following models:
- TODO: Name and the link to the model
When the model is trained, the following script shows how one can use the model:
TODO: Provide an example of how to use the modelIn case you use any of the components for your research, please refer to (and cite) the papers:
TODO: Paper
TODO: Related work
- Setup script
- Code for data preparation
- Code for model training
- Code for model validation
- Code for model evaluation
TODO: Acknowledgements