  This work describes the development of different models to detect patronising
and condescending language within extracts of news articles as part of the
SemEval 2022 competition (Task-4). This work explores different models based on
the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The
best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A
and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763.
