  With the popularity of social media, communications through blogs, Facebook,
Twitter, and other plat-forms have increased. Initially, English was the only
medium of communication. Fortunately, now we can communicate in any language.
It has led to people using English and their own native or mother tongue
language in a mixed form. Sometimes, comments in other languages have English
transliterated format or other cases; people use the intended language scripts.
Identifying sentiments and offensive content from such code mixed tweets is a
necessary task in these times. We present a working model submitted for Task2
of the sub-track HASOC Offensive Language Identification- DravidianCodeMix in
Forum for Information Retrieval Evaluation, 2020. It is a message level
classification task. An embedding model-based classifier identifies offensive
and not offensive comments in our approach. We applied this method in the
Manglish dataset provided along with the sub-track.
