| | --- |
| | 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} |
| | } |
| | ``` |
| |
|
