  We present working notes for DS@GT team in the eRisk 2024 for Tasks 1 and 3.
We propose a ranking system for Task 1 that predicts symptoms of depression
based on the Beck Depression Inventory (BDI-II) questionnaire using binary
classifiers trained on question relevancy as a proxy for ranking. We find that
binary classifiers are not well calibrated for ranking, and perform poorly
during evaluation. For Task 3, we use embeddings from BERT to predict the
severity of eating disorder symptoms based on user post history. We find that
classical machine learning models perform well on the task, and end up
competitive with the baseline models. Representation of text data is crucial in
both tasks, and we find that sentence transformers are a powerful tool for
downstream modeling. Source code and models are available at
\url{https://github.com/dsgt-kaggle-clef/erisk-2024}.
