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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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--- |
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# PropagandaDetection |
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The model is a Transformer network based on a DistilBERT pre-trained model. |
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The pre-trained model is fine-tuned on the SemEval 2023 Task 3 training dataset for the propaganda detection task. |
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### Hyperparameters : |
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Batch size = 16; |
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Learning rate = 2e-5; |
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AdamW optimizer; |
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Epochs = 4. |
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Accuracy = 90 % on SemEval 2023 test set. |
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## References |
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``` |
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@inproceedings{bangerter2023unisa, |
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title={Unisa at SemEval-2023 task 3: a shap-based method for propaganda detection}, |
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author={Bangerter, Micaela and Fenza, Giuseppe and Gallo, Mariacristina and Loia, Vincenzo and Volpe, Alberto and De Maio, Carmen and Stanzione, Claudio}, |
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booktitle={Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)}, |
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pages={885--891}, |
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year={2023} |
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} |
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``` |
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