--- for deepmoji, vary proportion of positive/negative, and the proportion of protected attributes as well (2 variables)
--- for biography, lets keep the data as it is, the class imbalance and gender imbalance in professions, we can just handle whatever is there in the dataset.
--- for deepmoji, vary proportion of positive/negative, and the proportion of protected attributes as well (2 variables) --- for biography, lets keep the data as it is, the class imbalance and gender imbalance in professions, we can just handle whatever is there in the dataset.