- Download the pretrained model, unzip it, and place the folder in the
saved_models/
directory - Install requirements using
pip install -r requirements.txt
(I would recommend creating a virtual environment before doing so) - Your input data needs to be a csv with a
text
column and the model can then generate predictions for it-- a sample data file is included in the repository assample_data.csv
- To generate predictions (if your dataset is named
sample_data.csv
, otherwise change accordingly), run the following command inside the main directory:python test_model.py -m saved_models/pol_clf_checkpoint -d sample_data.csv
- You should now have a
predictions.csv
file with a classifier output column namedprediction
- If you need the checkpoint files please reach out by email to anshumanc[at]usf[dot]edu.
- Available here
If you use our model/data, please cite the following papers:
-
Plain-text:
Askari, H., Chhabra, A., von Hohenberg, B. C., Heseltine, M., & Wojcieszak, M. (2024). Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts. arXiv preprint arXiv:2403.13362.
-
Bib:
title={Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts}, author={Askari, Hadi and Chhabra, Anshuman and von Hohenberg, Bernhard Clemm and Heseltine, Michael and Wojcieszak, Magdalena}, journal={arXiv preprint arXiv:2403.13362}, year={2024}}