  This paper describes our submissions for the Social Media Mining for Health
(SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification,
extraction and normalization of adverse drug effect (ADE) mentions in English
tweets (Task-1) and (2) Classification of COVID-19 tweets containing
symptoms(Task-6). Our approach for the first task uses the language
representation model RoBERTa with a binary classification head. For the second
task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the
pre-trained models for both tasks. The models are placed on top of a custom
domain-specific processing pipeline. Our system ranked first among all the
submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our
system obtained an F1-score of 50% with improvements up to +8% F1 over the
score averaged across all submissions. The BERTweet model achieved an F1 score
of 94% on SMM4H 2021 Task-6.
