  In social-media platforms such as Twitter, Facebook, and Reddit, people
prefer to use code-mixed language such as Spanish-English, Hindi-English to
express their opinions. In this paper, we describe different models we used,
using the external dataset to train embeddings, ensembling methods for
Sentimix, and OffensEval tasks. The use of pre-trained embeddings usually helps
in multiple tasks such as sentence classification, and machine translation. In
this experiment, we haveused our trained code-mixed embeddings and twitter
pre-trained embeddings to SemEval tasks. We evaluate our models on macro
F1-score, precision, accuracy, and recall on the datasets. We intend to show
that hyper-parameter tuning and data pre-processing steps help a lot in
improving the scores. In our experiments, we are able to achieve 0.886 F1-Macro
on OffenEval Greek language subtask post-evaluation, whereas the highest is
0.852 during the Evaluation Period. We stood third in Spanglish competition
with our best F1-score of 0.756. Codalab username is asking28.
