File size: 1,608 Bytes
3e889ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | Fine-tuned DistilBERT for binary depression classification from
social media text (Twitter + Reddit).
Trained as part of the ExplainDepression Pipeline — a research
system for explainable and fair depression detection using SHAP
attributions, Depression Explanation Score (DES), and Explanation
Consistency Index across Demographics (ECID).
━━━━━━━━━━━━━━━━━━━━━━━━━━━
PERFORMANCE
Accuracy → 96.17%
F1 Score → 0.9617
AUC-ROC → 0.9937
ECID → 0.2063 (bias detected in 3 clinical categories)
━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRAINING DETAILS
Base Model → distilbert-base-uncased
Epochs → 3
Batch Size → 64
Learning Rate → 3e-5
Max Length → 96 tokens
Train Size → 11,200 posts
Platform → Kaggle GPU T4
━━━━━━━━━━━━━━━━━━━━━━━━━━━
LABELS
LABEL_0 → Not Depressed
LABEL_1 → Depressed
━━━━━━━━━━━━━━━━━━━━━━━━━━━
HOW TO USE
from transformers import pipeline
clf = pipeline(
"text-classification",
model="mdsajjadullah/explainDepression-distilbert",
return_all_scores=True
)
result = clf("I feel hopeless and completely alone.")
# LABEL_1 score = depression probability
━━━━━━━━━━━━━━━━━━━━━━━━━━━
INTENDED FOR RESEARCH ONLY.
Not for clinical diagnosis or deployment without
professional mental health oversight. |