PropagandaDetection

The model is a Transformer network based on a DistilBERT pre-trained model. The pre-trained model is fine-tuned on the SemEval 2023 Task 3 training dataset for the propaganda detection task.

Hyperparameters :

Batch size = 16; Learning rate = 2e-5; AdamW optimizer; Epochs = 4.

Accuracy = 90 % on SemEval 2023 test set.

References

@inproceedings{bangerter2023unisa,
  title={Unisa at SemEval-2023 task 3: a shap-based method for propaganda detection},
  author={Bangerter, Micaela and Fenza, Giuseppe and Gallo, Mariacristina and Loia, Vincenzo and Volpe, Alberto and De Maio, Carmen and Stanzione, Claudio},
  booktitle={Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  pages={885--891},
  year={2023}
}