metadata
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
{
"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
from datasets import load_dataset
ds = load_dataset("Msduck/invoice_dataset")
print(ds)
print(ds["train"][0])