  Social networking platforms provide a conduit to disseminate our ideas, views
and thoughts and proliferate information. This has led to the amalgamation of
English with natively spoken languages. Prevalence of Hindi-English code-mixed
data (Hinglish) is on the rise with most of the urban population all over the
world. Hate speech detection algorithms deployed by most social networking
platforms are unable to filter out offensive and abusive content posted in
these code-mixed languages. Thus, the worldwide hate speech detection rate of
around 44% drops even more considering the content in Indian colloquial
languages and slangs. In this paper, we propose a methodology for efficient
detection of unstructured code-mix Hinglish language. Fine-tuning based
approaches for Hindi-English code-mixed language are employed by utilizing
contextual based embeddings such as ELMo (Embeddings for Language Models),
FLAIR, and transformer-based BERT (Bidirectional Encoder Representations from
Transformers). Our proposed approach is compared against the pre-existing
methods and results are compared for various datasets. Our model outperforms
the other methods and frameworks.
