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In social-media platforms such as Twitter, Facebook, and Reddit, peopleprefer to use code-mixed language such as Spanish-English, Hindi-English toexpress their opinions. In this paper, we describe different models we used,using the external dataset to train embeddings, ensembling methods forSentimix, and OffensEval tasks. The use of pre-trained embeddings usually helpsin multiple tasks such as sentence classification, and machine translation. Inthis experiment, we haveused our trained code-mixed embeddings and twitterpre-trained embeddings to SemEval tasks. We evaluate our models on macroF1-score, precision, accuracy, and recall on the datasets. We intend to showthat hyper-parameter tuning and data pre-processing steps help a lot inimproving the scores. In our experiments, we are able to achieve 0.886 F1-Macroon OffenEval Greek language subtask post-evaluation, whereas the highest is0.852 during the Evaluation Period. We stood third in Spanglish competitionwith our best F1-score of 0.756. Codalab username is asking28.

Voice@SRIB at SemEval-2020 Task 9 and 12: Stacked Ensembling method for Sentiment and Offensiveness detection in Social Media