Detecting cyberbullying in texts is an essential task as it curtails and identifies s ocial p roblems. I n t his p aper, we propose an architecture called Augmented BERT which combines both data augmentation techniques and BERT for detecting cyberbullying content in texts. Many techniques have been used in prior works to augment existing data for classification tasks and BERT had been applied in many text classification problems. However, there is a lack of annotated cyberbullying texts and obtaining annotated texts is hard and expensive. We propose to use various GAN-based and autoencoder-based data augmentation techniques to generate annotated data. The augmented texts can be used to fine-tune BERT. We choose to use HateBERT which is already pre-trained on abusive language to detect cyberbullying texts. Experimental results show an increased improvement over other cyberbullying detection models.