  The internet today has become an unrivalled source of information where
people converse on content based websites such as Quora, Reddit, StackOverflow
and Twitter asking doubts and sharing knowledge with the world. A major arising
problem with such websites is the proliferation of toxic comments or instances
of insincerity wherein the users instead of maintaining a sincere motive
indulge in spreading toxic and divisive content. The straightforward course of
action in confronting this situation is detecting such content beforehand and
preventing it from subsisting online. In recent times Transfer Learning in
Natural Language Processing has seen an unprecedented growth. Today with the
existence of transformers and various state of the art innovations, a
tremendous growth has been made in various NLP domains. The introduction of
BERT has caused quite a stir in the NLP community. As mentioned, when
published, BERT dominated performance benchmarks and thereby inspired many
other authors to experiment with it and publish similar models. This led to the
development of a whole BERT-family, each member being specialized on a
different task. In this paper we solve the Insincere Questions Classification
problem by fine tuning four cutting age models viz BERT, RoBERTa, DistilBERT
and ALBERT.
