giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI
Harshul Surana, Basavraj Chinagundi · 2022
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 binary classifier.Our best result on the leaderboard achieves a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English.We rank fourth in the English task.We have opensourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.