Hugging Face model: rockCO78/ai-security-crosswalk-vfinal
Author: rockCO78
Created: 2026-04-25 06:37:36
Last modified: 2026-04-25 06:52:02
Pipeline: text-classification
Likes: 2 | Downloads: 0
Tags: safetensors, security, ai-security, crosswalk, ordinal-classification, ensemble, pytorch, text-classification, en, dataset:custom, license:apache-2.0, model-index, region:us
Card metadata:
license: apache-2.0
pipeline_tag: text-classification
tags: security, ai-security, crosswalk, ordinal-classification, ensemble, pytorch
datasets: custom
language: en
metrics: f1, accuracy
description: 3-model ensemble (RoBERTa-large, DeBERTa-v3-base, BGE-large-v1.5) for ordinal tier classification (Unrelated/Partial/Related/Equivalent) of AI security control pairs across frameworks including NIST AI RMF, OWASP AI, MITRE ATLAS, ISO/IEC 42001, and EU AI Act. Built by Rock Lambros at University of Denver.
primaryPurpose: MACHINE-LEARNING
typeOfModel: ensemble-transformer-classifier
domain: ai-security
autonomyType: assistive
intendedUse: Scoring cross-framework AI security control mappings; assigning ordinal relationship tiers (Unrelated/Partial/Related/Equivalent) to control pairs for framework alignment analysis.
informationAboutApplication: Primary use is scoring cross-framework control mappings for AI security frameworks. Out of scope: general NLI, non-security domains, or applications without human review.
informationAboutTraining: 5,920 expert-labeled pairs from 9 AI security frameworks. Mapping-level deduplication removed 56% contamination. Ordinal losses (KL, CORN, focal). 3x H100 80GB GPUs, BF16 mixed precision, ~4 hours.
modelDataPreprocessing: Mapping-level deduplication to remove cross-split leakage; ordinal soft-label generation; class-balanced sampling for rare EQUIVALENT tier.
hyperparameter: AdamW optimizer; linear warmup + cosine decay; per-model learning rates tuned via Optuna; BF16 mixed precision; max_length=512 tokens
ethicalConsiderations: No personal data used. Security-domain only. Outputs are advisory scores requiring human review before compliance or procurement use. Single expert labeler; no formal inter-annotator agreement measured.
technicalLimitations: Small test set (179 pairs, 7 EQUIVALENT examples); English-only; trained on 9 specific frameworks; PARTIAL/RELATED boundary ambiguity yields lowest F1 scores.
safetyRiskAssessment: Outputs are advisory scores, not authoritative compliance determinations. Human expert review required before use in compliance decisions, procurement, or framework adoption. No personal data processed.
energyConsumption: ~8.4 kWh estimated for training (~3.4 kg CO2e at US average grid intensity)
energyQuantity: 8.4
energyUnit: kWh
metric: exact_accuracy=0.799; adjacent_accuracy=0.922; macro_f1=0.558; per-class F1: UNRELATED=0.928, PARTIAL=0.526, RELATED=0.378, EQUIVALENT=0.400
metricDecisionThreshold: Conformal prediction sets calibrated at 90% coverage target; all classes achieve >90% empirical coverage
modelExplainability: Softmax probability distributions over 4 tiers provide interpretable confidence scores; conformal prediction sets provide coverage-guaranteed prediction intervals
useSensitivePersonalInformation: No
standardCompliance: CycloneDX 1.6; AIBOM (OWASP GenAI Security Project)
paper: https://huggingface.co/rockCO78/ai-security-crosswalk-vfinal
external_references: type: website
url: https://huggingface.co/rockCO78/ai-security-crosswalk-vfinal, type: distribution
url: https://huggingface.co/rockCO78/ai-security-crosswalk-vfinal/tree/main