  The number of increased social media users has led to a lot of people
misusing these platforms to spread offensive content and use hate speech.
Manual tracking the vast amount of posts is impractical so it is necessary to
devise automated methods to identify them quickly. Large language models are
trained on a lot of data and they also make use of contextual embeddings. We
fine-tune the large language models to help in our task. The data is also quite
unbalanced; so we used a modified cross-entropy loss to tackle the issue. We
observed that using a model which is fine-tuned in hindi corpora performs
better. Our team (HNLP) achieved the macro F1-scores of 0.808, 0.639 in English
Subtask A and English Subtask B respectively. For Hindi Subtask A, Hindi
Subtask B our team achieved macro F1-scores of 0.737, 0.443 respectively in
HASOC 2021.
