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    <abstract>To precisely identify the different forms of online sexism, we utilize several sentence transformer models such as ALBERT, BERT, RoBERTa, DistilBERT, and XLNet. By combining the predictions from these models, we can generate a more comprehensive and improved result. Each transformer model is trained after pre-processing the data from the training dataset, ensuring that the models are effective at detecting and classifying instances of online sexism. For Task A, the model had to classify the texts as sexist or not sexist. We implemented ALBERT, an NLP-based sentence transformer. For task B, we implemented BERT, RoBERTa, DistilBERT and XLNet and took the mode of predictions for each text as the final prediction for the given text. For task C, we implemented ALBERT, BERT, RoBERTa, DistilBERT and XLNet and took the mode of predictions as the final prediction for the given text.</abstract>
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%0 Conference Proceedings
%T PoSh at SemEval-2023 Task 10: Explainable Detection of Online Sexism
%A Sriram, Shruti
%A Chandran, Padma Pooja
%A M R, Shrijith
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sriram-etal-2023-posh
%X To precisely identify the different forms of online sexism, we utilize several sentence transformer models such as ALBERT, BERT, RoBERTa, DistilBERT, and XLNet. By combining the predictions from these models, we can generate a more comprehensive and improved result. Each transformer model is trained after pre-processing the data from the training dataset, ensuring that the models are effective at detecting and classifying instances of online sexism. For Task A, the model had to classify the texts as sexist or not sexist. We implemented ALBERT, an NLP-based sentence transformer. For task B, we implemented BERT, RoBERTa, DistilBERT and XLNet and took the mode of predictions for each text as the final prediction for the given text. For task C, we implemented ALBERT, BERT, RoBERTa, DistilBERT and XLNet and took the mode of predictions as the final prediction for the given text.
%R 10.18653/v1/2023.semeval-1.177
%U https://aclanthology.org/2023.semeval-1.177/
%U https://doi.org/10.18653/v1/2023.semeval-1.177
%P 1276-1281

Markdown (Informal)

[PoSh at SemEval-2023 Task 10: Explainable Detection of Online Sexism](https://aclanthology.org/2023.semeval-1.177/) (Sriram et al., SemEval 2023)

• PoSh at SemEval-2023 Task 10: Explainable Detection of Online Sexism (Sriram et al., SemEval 2023)

ACL

• Shruti Sriram, Padma Pooja Chandran, and Shrijith M R. 2023. PoSh at SemEval-2023 Task 10: Explainable Detection of Online Sexism. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1276–1281, Toronto, Canada. Association for Computational Linguistics.