| Fine-tuned DistilBERT for binary depression classification from |
| social media text (Twitter + Reddit). |
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| 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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| ━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
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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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| ━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
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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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| ━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
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| LABELS |
| LABEL_0 → Not Depressed |
| LABEL_1 → Depressed |
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| ━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
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| HOW TO USE |
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|
| 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. |
