This repository contains our notebooks for the train, test and use STACC, our submission to the NLBSE'23 Code Comment Classification tool competition.
Install SetFit with the Optuna backend:
pip install setfit[optuna]
Now you're ready to roll
- 1-Model_selection.ipynb outlines how we selected the base model and tuned the hyperparameters
- 2-Creating_classifiers.ipynb shows how we created the 19 classifiers that make up STACC π
- 3-Inference.ipynb shows how you can put STACC to good use by loading a classifier and start predicting.
If you wish to run the notebooks in Google Colab we provide the following ready-to-go notebooks:
We also integrated STACC π in a Huggingface space.
@software{STACC_2023,
author = {Al-Kaswan, Ali and Izadi, Maliheh and van Deursen, Arie},
year = {2023},
title = {STACC: a set of SentenceTransformer Assisted Comment Classifiers},
url = {https://github.com/AISE-TUDelft/STACC},
version = {1.0}