In this paper we describe a system submit- ted to the INLG 2022 Generation Challenge (GenChal) on Quality Evaluation of the Low- Resource Synthetically Generated Code-Mixed Hinglish Text. We implement a Bi-LSTM- based neural network model to predict the Av- erage rating score and Disagreement score of the synthetic Hinglish dataset. In our mod- els, we used word embeddings for English and Hindi data, and one hot encodings for Hinglish data. We achieved a F1 score of 0.11, and mean squared error of 6.0 in the average rating score prediction task. In the task of Disagreement score prediction, we achieve a F1 score of 0.18, and mean squared error of 5.0.