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---
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}
}
```
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