| ---
|
| license: mit
|
| task_categories:
|
| - tabular-classification
|
| - text-classification
|
| language:
|
| - ar
|
| - en
|
| tags:
|
| - synthetic-data
|
| - gcc
|
| - invoices
|
| - ecommerce
|
| - erp
|
| - fintech
|
| - testing
|
| - data-quality
|
| - arabic
|
| - benchmark
|
| pretty_name: GCC-InvoiceMath-Verified
|
| size_categories:
|
| - n<1K
|
| ---
|
|
|
| # GCC-InvoiceMath-Verified
|
|
|
| A deterministic synthetic benchmark for testing invoice arithmetic, numeric parsing, rounding, and claimed-total validation across six GCC currencies.
|
|
|
| ## Overview
|
|
|
| GCC-InvoiceMath-Verified contains **600 records / 300 causal pairs**.
|
|
|
| Each pair contains:
|
|
|
| - one mathematically correct invoice
|
| - one counterfactual invoice where exactly one claimed total differs by one minor currency unit
|
|
|
| The dataset is intended for:
|
|
|
| - ERP QA
|
| - e-invoicing integration testing
|
| - financial software regression testing
|
| - structured-output validation
|
| - numerical reasoning evaluation
|
| - synthetic data benchmarking
|
|
|
| This benchmark is **not a tax-law compliance benchmark**.
|
|
|
| It does not certify compliance with ZATCA, UAE e-invoicing rules, PINT, UBL, or any national tax regulation.
|
|
|
| ## Countries and currencies
|
|
|
| | Country | Currency | Records |
|
| |---|---|---:|
|
| | Saudi Arabia | SAR | 100 |
|
| | United Arab Emirates | AED | 100 |
|
| | Qatar | QAR | 100 |
|
| | Kuwait | KWD | 100 |
|
| | Bahrain | BHD | 100 |
|
| | Oman | OMR | 100 |
|
|
|
| SAR, AED and QAR use two benchmark fractional digits.
|
|
|
| KWD, BHD and OMR use three benchmark fractional digits.
|
|
|
| ## Scenario families
|
|
|
| The benchmark contains 10 deterministic scenario families:
|
|
|
| 1. `half_up_tie`
|
| 2. `line_vs_document_rounding`
|
| 3. `line_allowance`
|
| 4. `line_charge`
|
| 5. `prepayment`
|
| 6. `credit_note`
|
| 7. `large_amount_stress`
|
| 8. `zero_rate`
|
| 9. `mixed_rates`
|
| 10. `zero_due`
|
|
|
| Each family contains **60 records**.
|
|
|
| ## Numeric rendering
|
|
|
| Four rendering styles are equally represented:
|
|
|
| - ASCII
|
| - grouped ASCII
|
| - Arabic-Indic digits
|
| - grouped Arabic-Indic digits
|
|
|
| Each style contains **150 records**.
|
|
|
| This makes it possible to test parsers handling both Western and Arabic-Indic numeric representations.
|
|
|
| ## Dataset structure
|
|
|
| Important fields include:
|
|
|
| ```json
|
| {
|
| "id": "...",
|
| "pair_id": "...",
|
| "split": "train",
|
| "country": "SA",
|
| "family": "half_up_tie",
|
| "contract_id": "gcc-invoice-math-1.0",
|
| "invoice": {
|
| "currency": "SAR",
|
| "document_type": "invoice",
|
| "numeric_style": "arabic_indic",
|
| "lines": [],
|
| "prepaid": "...",
|
| "claimed_totals": {}
|
| },
|
| "expected": {
|
| "valid": true,
|
| "totals": {},
|
| "error_fields": []
|
| },
|
| "mutation": null,
|
| "fingerprint_sha256": "..."
|
| }
|
| Ground truth
|
| Ground truth is generated procedurally using Python Decimal arithmetic with explicit ROUND_HALF_UP behavior.
|
| Validation is performed independently using fractions.Fraction.
|
| The validator does not import generator code.
|
| This provides two different arithmetic implementations:
|
| - generator oracle: Decimal
|
| - validation oracle: rational arithmetic
|
| Validation results
|
| - Records generated: 600
|
| - Records validated: 600
|
| - Pass rate: 100%
|
| - Valid examples: 300
|
| - Invalid examples: 300
|
| - Unique causal pairs: 300
|
| The release also checks:
|
| - JSON Schema compliance
|
| - unique IDs
|
| - unique semantic source invoices
|
| - fingerprints
|
| - country/currency consistency
|
| - pair integrity
|
| - train/validation/test pair isolation
|
| - exactly one causal mutation per invalid record
|
| - exactly one minor-unit difference
|
| - generator/validator oracle agreement
|
| Splits
|
| - train: 360
|
| - validation: 120
|
| - test: 120
|
| Positive and negative members of a causal pair are always kept in the same split.
|
| Synthetic provenance
|
| All records are procedurally generated.
|
| - no real invoices
|
| - no PII
|
| - no copied commercial records
|
| - no real tax identifiers
|
| - no customer transaction data
|
| External sources were used only for background research and currency-precision verification.
|
| Important limitations
|
| This dataset does not cover:
|
| - XML / UBL validation
|
| - digital signatures
|
| - QR codes
|
| - live tax submission
|
| - regulatory certification
|
| - real tax IDs
|
| - OCR images
|
| - exchange rates
|
| - payment reconciliation
|
| - jurisdiction-specific VAT interpretation
|
| Tax rates used in the benchmark are synthetic testing parameters.
|
| Intended use
|
| Recommended uses:
|
| - unit tests
|
| - regression tests
|
| - ERP adapter QA
|
| - invoice parser evaluation
|
| - financial-agent evaluation
|
| - data-quality pipelines
|
| - numeric-reasoning benchmarks
|
| Commercial customization
|
| Need a private benchmark matching your ERP, invoice schema, accounting rules, API, or failure modes?
|
| Custom synthetic datasets can be generated with:
|
| - client-specific field mappings
|
| - custom currencies
|
| - custom rounding behavior
|
| - additional failure modes
|
| - private held-out regression suites
|
| - custom JSON / CSV / Parquet / SQL formats
|
| - validation scripts
|
| - QA reports
|
| The commercial value is in adapting the benchmark to a real system, not simply generating more generic rows.
|
| Reproducibility
|
| The generator is deterministic and seed-based.
|
| Default seed:
|
| 20260930
|
| See generator.py and validator.py.
|
| License
|
| MIT License for the original benchmark code and synthetic fixtures.
|
| See LICENSE.
|
| |
| --- |
| |
| ## RegalFire — Custom / Private Dataset Work |
| |
| RegalFire builds custom AI datasets, evaluation sets and data pipelines for: |
| |
| - AI agents |
| - computer-use systems |
| - multimodal models |
| - world models |
| - RAG systems |
| - code agents |
| - enterprise AI |
| |
| Available services include: |
| |
| - synthetic data generation |
| - private evaluation datasets |
| - agent trajectories |
| - failure / recovery datasets |
| - multimodal RGB / segmentation / state-action data |
| - web data acquisition |
| - cleaning and deduplication |
| - structured dataset packaging |
| - continuous dataset production |
| |
| For custom or private work: |
| |
| **Email: ootiris@gmail.com** |
| |
| Hugging Face: **RegalFire**
|
| |
