giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI

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Harshul Surana, Basavraj Chinagundi

Published: 2022, Last Modified: 09 Mar 2026LT-EDI 2022EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper describes team giniUs’ submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022. We have fine-tuned the Roberta-large pre-trained model and extracted the last four decoder layers to build a classifier. Our best result on the leaderboard achieve a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English. We have secured a rank of 4 for the English task. We have open-sourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.