---
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
---

# 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}
}
```