Early detection of suicidal ideations is one of the key suicide prevention strategies. However, there are challenges that obstruct the detection of suicidal ideations. Mainly, the stigma surrounding mental health, and suicide in particular, obstructs traditional risk screening methods, such as questionnaires and interviews. These methods rely on at-risk individuals to explicitly communicate their suicidal thoughts. At the same time, people with suicidal ideations are increasingly turning to online forums such as Reddit to share their experiences and seek emotional support. Consequently, these platforms have emerged as a large source of textual data for detecting suicidal ideations using machine learning and natural language processing methods. This paper aims to explore the effectiveness of transformer-based models for detecting suicidal ideations on Reddit forums. In this study, the transformer models were fine-tuned and compared against machine learning and deep learning baselines. Our experimental results show that the fine-tuned base-BERT transformer model demonstrates superior performance in detecting suicidal ideations compared to baseline machine learning and deep learning models, achieving an F1-score of 99%.