Kumaresan, P.K.PremjithSakuntharaj, R.Thavareesan, S.Subalalitha, S.Anand Kumar, M.Chakravarthi, B.R.Mccrae, J.P.2026-02-062021ACM International Conference Proceeding Series, 2021, Vol., , p. 16-1821531633https://doi.org/10.1145/3503162.3503179https://idr.nitk.ac.in/handle/123456789/30103We present the results of HASOC-Dravidian-CodeMix shared task1 held at FIRE 2021, a track on offensive language identification for Dravidian languages in Code-Mixed Text in this paper. This paper will detail the task, its organisation, and the submitted systems. The identification of offensive language was viewed as a classification task. For this, 16 teams participated in identifying offensive language from Tamil-English code mixed data, 11 teams for Malayalam-English code mixed data and 14 teams for Tamil data. The teams detected offensive language using various machine learning and deep learning classification models. This paper has analysed those benchmark systems to find out how well they accommodate a code-mixed scenario in Dravidian languages, focusing on Tamil and Malayalam. © 2021 Owner/Author.datasetsdeep learningevaluationHate speech