metadata
language:
- en
license: cc-by-4.0
tags:
- layoutlmv3
- token-classification
- invoice-extraction
- document-ai
- invoiceguard
datasets:
- Kwash67/layoutlmv3-invoice-dataset
metrics:
- seqeval
🛡️ InvoiceGuard LayoutLMv3 Invoice Extractor
Fine-tuned microsoft/layoutlmv3-base model for multimodal token classification and Key Information Extraction (KIE) on commercial invoices.
Model Details
- Base Architecture:
microsoft/layoutlmv3-base - Task: Multimodal Document Token Classification
- Dataset:
Kwash67/layoutlmv3-invoice-dataset(~2,043 train / 70 val / 125 test) - Training Epochs: 12 (lr=3e-5, warmup=10%, batch=4, grad_acc=2)
- Mixed Precision: fp16
Test Set Metrics (Seqeval Entity-Level)
| Metric | Score |
|---|---|
| Overall F1 | 0.7244 |
| Overall Precision | 0.7167 |
| Overall Recall | 0.7324 |
InvoiceGuard Schema Mapping
Maps dataset entities to 21 canonical InvoiceGuard fields:
invoice_number, invoice_date, due_date, seller, client, seller_tax_id, client_tax_id, iban, item_desc, item_qty, item_net_price, item_net_worth, item_vat, item_gross_worth, subtotal, total_vat, total_gross_worth, currency, discount, shipping, payment_terms.
Limitations
- Optimized for invoices with bounding boxes normalized to
[0..1000]. - Highly dependent on OCR word-level segmentation accuracy.