Recent years saw a dramatic increase in the popularity of online counseling services providing emergency mental health support. This paper provides a new language model for automatic detection of suicide risk in online chat sessions between help-seekers and counselors. The model adapts a hierarchical BERT language model for this task. It extends the state of the art in capturing aspects of the conversation structure in the counseling session and in integrating psychological theory into the model. We test the performance of our approach in a leading national online counseling service that operates in the Hebrew language. Our model outperformed other non-hierarchical approaches from the literature, achieving a 0.76 F2 score and 0.92 ROC-AUC. Moreover, we demonstrate our model’s superiority over strong baselines even early on in the conversation, which is key for real-time detection in the field. This is a first step towards incorporating suicide predictive models in online support services and advancing NLP tools for resource-bounded languages.