SentIDP Invoice NER is a named entity recognition model for automated extraction of key fields from Ghanaian business invoices. It is fine-tuned on top of albertosei/invoice-ner-v2-amaye15, which was originally trained on 13,000 invoice images using docTR OCR.

This model was developed by the TYNCAD team as part of an Intelligent Document Processing pipeline tailored specifically to the Ghanaian business context — including GRA tax rules, local company naming conventions, Ghanaian banks, Mobile Money (MoMo), and GHS currency formats.

Training Data

20,000 synthetic Ghanaian invoices
15 industries covered: logistics, pharmacy, real estate, healthcare, insurance, branding, banking, fintech, automobile, marketing, agencies, NGO, government, technology, financial
8 currencies with GHS (Ghana Cedis) at 62% weight
10 PDF layout engines for visual diversity
Ground truth generated automatically via PyMuPDF — exact word-level bounding boxes, zero manual labelling
Augmented with scan noise, rotation, perspective distortion, and JPEG compression artifacts to simulate real-world scans

Training Configuration

Parameter	Value
Base model	albertosei/invoice-ner-v2-amaye15
Epochs completed	2 of 3
Train samples	18,000
Validation samples	2,000
Batch size	2 (gradient accumulation: 2)
Learning rate	5e-5
Weight decay	0.01
Max sequence length	512
Precision	fp16
Hardware	2× NVIDIA Tesla T4 (Kaggle)

Evaluation Results

Evaluated on 2,000 held-out validation invoices after each epoch:

Epoch	Training Loss	Validation Loss	F1	Precision	Recall
1	0.0361	0.0170	0.9974	0.9972	0.9975
2	0.0197	0.0095	0.9988	0.9991	0.9985

Labels (50 total)

The model extracts the following fields using BIO tagging:

Category	Labels
Document header	HEADER_TYPE, HEADER_NUMBER, HEADER_DATE, DUE_DATE, PO_NUMBER
Seller	SELLER_NAME, SELLER_ADDRESS, SELLER_TIN
Buyer	BUYER_NAME, BUYER_ADDRESS
Line items	ITEM_DESC, QTY, UNIT_PRICE, LINE_TOTAL
Financials	SUBTOTAL, TAX_AMOUNT, GRAND_TOTAL, AMOUNT_WORDS
Payment	PAYMENT_TERMS, BANK_NAME, ACCOUNT_NUMBER, MOMO_DETAILS
Signatories	PREPARED_BY, AUTHORISED_BY
Other	O, B-OTHER

Intended Use

Automated invoice processing for Ghanaian businesses
GRA tax compliance validation
Accounts payable automation
Invoice data extraction for ERP integration

Limitations

Trained entirely on synthetic data — v1.0
Performance on heavily degraded real scans may vary
Real Ghanaian invoice fine-tuning planned for v2.0
Currently supports single-page invoices only

Roadmap

v1.1 — Epoch 3 training completion
v2.0 — Fine-tuning on real annotated Ghanaian invoices
v2.1 — Multi-page invoice support
v3.0 — Full IDP pipeline with structured JSON output

Developed By

TYNCAD

Citation

If you use this model, please cite:

@misc{sentidp-invoice-ner-2025,
  title     = {SentIDP Invoice NER — Ghanaian Invoice Information Extraction},
  author    = {TYNCAD},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/albertosei/sentidp-ner_20k}
}