BERT NLP Model for Depression Detection
BERT fine‑tuned to detect depression/suicidal ideation signals from text — reproducible NLP training + evaluation pipeline.
Overview
A complete NLP research pipeline that fine‑tunes BERT to classify mental‑health related text. The repository includes code + dataset artifacts for repeatable training, inference, and evaluation.
Problem
Mental‑health signals in text are nuanced, context‑dependent, and easy to misclassify. The challenge is building a model that captures semantics beyond keywords while remaining measurable and reproducible.
Approach
Prepared a clean training pipeline: dataset handling, text normalization, tokenizer + BERT fine‑tuning, and structured evaluation. Ran experiments to validate performance across categories and ensured the workflow is easy to rerun and extend. Documented the project clearly so reviewers can understand assumptions, limitations, and next steps.
Impact
Demonstrates practical transformer fine‑tuning and research discipline. The outcome is a strong portfolio artifact at the intersection of NLP and social impact — designed for extension into safer, real‑world screening tools.