  This paper presents models created for the Social Media Mining for Health
2023 shared task. Our team addressed the first task, classifying tweets that
self-report Covid-19 diagnosis. Our approach involves a classification model
that incorporates diverse textual augmentations and utilizes R-drop to augment
data and mitigate overfitting, boosting model efficacy. Our leading model,
enhanced with R-drop and augmentations like synonym substitution, reserved
words, and back translations, outperforms the task mean and median scores. Our
system achieves an impressive F1 score of 0.877 on the test set.
