text-generation
Collection
3 items
β’
Updated
Domain-Specific AI for Malaysian HR compliance with specialized capabilities in:
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": "β οΈ Requires HRD Corp review"
}
}
graph TD
A[Dispute Reported] --> B{Type?}
B -->|Unfair Dismissal| C[IRA1967 Sec. 20]
B -->|Harassment| D[POHA 2022]
C --> E[Generate Conciliation Proposal]
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()}
Composition:
Bias Mitigation:
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 |
Transparency Measures:
Limitations:
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")
@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}
}
Base model
moonshotai/Kimi-K2-Instruct