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