We present a system to identify tweets about the COVID19 disease outbreak that are deemed to be informative on Twitter for use in downstream applications. The system scored a F1-score of 0.8941, Precision of 0.9028, Recall of 0.8856 and Accuracy of 0.9010. In the shared task organized as part of the 6th Workshop of Noisy User-generated Text (WNUT), the system was ranked 18th by F1-score and 13th by Accuracy.
Recommended citation: Arjun Magge*, Varad Pimpalkhute*, Divya Rallapalli, David Siguenza and G. Gonzalez-Hernandez, "e;Transformer models for classification of COVID19 posts on Twitter,"e; Proceedings of the SixthWorkshop on Noisy User-generated Text (W-NUT 2020), pp. 378–382. [DOI / Link / PDF] (* Equal Contribution)