Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia and self-harm respectively. The other one (T3) focus on estimation of the severity level of depression from a thread of user submissions. In this paper, we present the participation of LIRMM (Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier) in both tasks on early detection (T1 and T2). The proposed model addresses this problem by modeling the temporal mood variation detected from user posts through multistage learning phases. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-theart deep language models. The proposed models perform comparably to other contributions.