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Malaysian HR Assistant 2025 (MYHRA-2025) πŸ‡²πŸ‡Ύ

Model Overview

Domain-Specific AI for Malaysian HR compliance with specialized capabilities in:

  • Wage disparity analysis
  • Payroll/EPF/SOCSO calculations
  • Industrial relations guidance
  • Multilingual HR support

Core Capabilities

1. Wage Equity Analysis

Feature Legal Basis Accuracy
Gender Pay Gap Detection Pay Equality Act 2024 92%
Ethnicity Variance Alerts EA1955 Sec. 60L 88%
Disability Pay Compliance PDPA 2010 90%

Example Output:

{
  "analysis_type": "wage_disparity",
  "results": {
    "gender_gap": "18.2%",
    "high_risk_roles": ["Senior Manager", "Operations Executive"],
    "compliance_status": "⚠️ Requires HRD Corp review"
  }
}

2. Industrial Relations

graph TD
    A[Dispute Reported] --> B{Type?}
    B -->|Unfair Dismissal| C[IRA1967 Sec. 20]
    B -->|Harassment| D[POHA 2022]
    C --> E[Generate Conciliation Proposal]

3. Payroll Compliance

2025 Calculation Engine:

def calculate_epf(salary: float) -> dict:
    rates = {
        'employee': 0.11 if salary <= 5000 else 0.12,
        'employer': 0.13 if salary <= 5000 else 0.12
    }
    return {k: v*salary for k,v in rates.items()}

Training Data

Composition:

  • 45% Legal texts (Acts/Regulations)
  • 30% Wage records (Anonymized)
  • 15% Industrial court cases
  • 10% Multilingual Q&A

Bias Mitigation:

  • Debiased using MOHR's 2025 Equity Guidelines
  • Balanced representation across:
    • Gender
    • Ethnicity
    • Disability status

Performance

Task Dataset Metric Score
Wage Gap Detection MOHR Audit Cases F1 0.91
EPF Calculation KWSP Test Samples Accuracy 99.2%
Malay Legal QA MYCourt Bench EM 0.88

Ethical Considerations

Transparency Measures:

  • All wage analyses include confidence intervals
  • Legal citations for every compliance recommendation
  • Opt-out for employee data processing

Limitations:

  • Cannot process handwritten payslips
  • Manglish support limited to common HR phrases
  • East Malaysia labor laws require manual review

Usage

from transformers import pipeline

hr_analyzer = pipeline(
    "text-generation",
    model="chemmara/MYHRA-2025",
    trust_remote_code=True
)

# Wage disparity check
response = hr_analyzer("Analyze gender pay gap in Finance Department")

Citation

@model{myhra2025,
  title = {Malaysian HR Assistant 2025},
  author = {Chemmara Space Legal AI Team},
  year = {2025},
  version = {3.0.1},
  url = {https://huggingface.co/chemmara/MYHRA-2025}
}

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