| license: apache-2.0 | |
| task_categories: | |
| - text-generation | |
| - text-classification | |
| language: | |
| - en | |
| - hi | |
| tags: | |
| - invoice-extraction | |
| - document-ai | |
| - pydantic-schema | |
| - finetune | |
| - chatml | |
| size_categories: | |
| - 1K<n<10K | |
| # MindMap Enterprise Invoice Extraction & Fine-Tuning Dataset | |
| Production-grade benchmark and fine-tuning dataset built from **1,007 enterprise PDF invoices** for extracting structured JSON metadata matching strict target Pydantic schemas. | |
| ## Dataset Structure & Splits | |
| - `train` (1,074 examples): 80% training split containing English and Hindi Devanagari invoice document pairs. | |
| - `validation` (134 examples): 10% validation split. | |
| - `test_golden` (135 examples): 10% held-out test split for automated F1 and schema validity scoring. | |
| - `catastrophic_forgetting_benchmark` (5 examples): General capability benchmark suite (Math, Coding, Reasoning). | |
| ## Target Schema Specifications | |
| ```json | |
| { | |
| "invoice_number": "string", | |
| "vendor_name": "string", | |
| "invoice_date": "YYYY-MM-DD", | |
| "line_items": [ | |
| { | |
| "description": "string", | |
| "quantity": "number", | |
| "unit_price": "number" | |
| } | |
| ], | |
| "subtotal": "number", | |
| "tax_amount": "number", | |
| "total_amount": "number", | |
| "currency": "string (ISO 4217)" | |
| } | |
| ``` | |
| ## Quick Usage with Hugging Face Datasets | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("Msduck/invoice_dataset") | |
| print(ds) | |
| print(ds["train"][0]) | |
| ``` | |