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

Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language