Social media analytics are widely being explored by researchers for various applications. Prominent among them are identifying and blocking abusive contents especially targeting individuals and communities, for various reasons. The increasing abusive contents and the increasing number of users on social media demands automated tools to detect and filter the abusive contents as it is highly impossible to handle this manually. To address the challenges of detecting abusive contents, this paper describes the approaches proposed by our team MUCIC for Multilingual Gender Biased and Communal Language Identification shared task (ComMA@ICON) at International Conference on Natural Language Processing (ICON) 2021. This shared task dataset consists of code-mixed multi-script texts in Meitei, Bangla, Hindi as well as in Multilingual (a combination of Meitei, Bangla, Hindi, and English). The shared task is modeled as a multi-label Text Classification (TC) task combining word and char n-grams with vectors obtained from Multilingual Sentence Encoder (MSE) to train the Machine Learning (ML) classifiers using Pre-aggregation and Post-aggregation of labels. These approaches obtained the highest performance in the shared task for Meitei, Bangla, and Multilingual texts with instance-F1 scores of 0.350, 0.412, and 0.380 respectively using Pre-aggregation of labels.