model_index.json

{
  "description": "DistilBERT-based 3-class classifier for clinical text, distinguishing between RISK: HIGH, RISK: LOW, and ATTACK: DETECTED.",
  "model_name": "Robust-Clinical-Risk-Detector",
  "pipeline_tag": "text-classification",
  "tags": [
    "distilbert",
    "healthcare",
    "clinical-nlp",
    "risk-assessment",
    "adversarial-robustness",
    "pytorch"
  ],
  "datasets": [
    "Health/perturbed_ehr_notes"
  ],
  "metrics": [
    {
      "name": "Clean Accuracy",
      "type": "accuracy",
      "value": 0.956
    },
    {
      "name": "Adversarial Robustness (A-ACC)",
      "type": "attack_accuracy",
      "value": 0.781
    }
  ]
}