Update README.md

README.md

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-license: mit
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+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.