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).

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PERFORMANCE
  Accuracy  →  96.17%
  F1 Score  →  0.9617
  AUC-ROC   →  0.9937
  ECID      →  0.2063 (bias detected in 3 clinical categories)

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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

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LABELS
  LABEL_0  →  Not Depressed
  LABEL_1  →  Depressed

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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

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INTENDED FOR RESEARCH ONLY.
Not for clinical diagnosis or deployment without 
professional mental health oversight.