Fine-tuned bert-base-uncased for 5-class mental health classification of social media posts. Developed as part of a COM748 Masters Research Project: Explainable Transformer Based Framework for Early Detection of Mental Health Disorders from Social Media Text.

Classes

ID	Label
0	Anxiety/Stress
1	Bipolar
2	Depression
3	Normal
4	Suicidal

Performance (held-out test set, n = 7,106)

Metric	Score
Accuracy	83.4%
Macro F1	0.841

Per-class F1: Normal 0.953 · Anxiety/Stress 0.882 · Bipolar 0.858 · Depression 0.770 · Suicidal 0.742

For comparison, the same encoder used as a frozen feature extractor reached only 0.680 macro-F1, and a TF-IDF + XGBoost baseline reached 0.775.

Training data

~47,400 posts merged from two public Kaggle datasets: Sentiment Analysis for Mental Health and Depression: Reddit Dataset (Cleaned), deduplicated and mapped to 5 classes, split 70/15/15.

Training setup

3 epochs, batch size 16, max length 128, AdamW lr 2e-5, weight decay 0.01
Linear warmup (10%) then linear decay, gradient clipping at 1.0
Class-weighted cross-entropy to counter class imbalance
Best checkpoint selected by validation macro-F1

Usage

python

from transformers import pipeline

clf = pipeline("text-classification", model="usman-isb/bert-mental-health-detection")
print(clf("lately I cannot stop worrying about everything, my heart races all day"))

Intended use & limitations

⚠️ Research prototype only. This model is not a clinical diagnostic instrument and must not be used as a substitute for professional mental health assessment. Labels reflect patterns in self-reported social media text, not clinical diagnoses. Performance on text from other platforms, languages, or demographics is untested.

code-world/bert-mental-health-detection

BERT for Early Detection of Mental Health Disorders from Social Media Text