This paper presents our approach to the RANLP 2025 Shared Task on “Identification of the Severity of Depression in Forum Posts.” The objective of the task is to classify user-generated posts into one of four severity levels of depression: subthreshold, mild, moderate, or severe. A key challenge in the task was the absence of annotated training data. To address this, we employed a two-stage pipeline: first, we used zero-shot classification with facebook/bart-large-mnli to generate pseudo-labels for the unlabeled training set. Next, we fine-tuned a DistilBERT model on the pseudo-labeled data for multi-class classification. Our system achieved an internal accuracy of 0.92 on the pseudo-labeled test set and an accuracy of 0.289 on the official blind evaluation set. These results demonstrate the feasibility of leveraging zero-shot learning and weak supervision for mental health classification tasks, even in the absence of gold-standard annotations.