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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])
```
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