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Belegant

BelegBench

🇨🇭 CH · 🇩🇪 DE · 🇦🇹 AT — Belege in DE / FR / IT

BelegBench ist der Benchmark hinter Belegant — der On-Prem Beleg-AI für den DACH-Raum. Von den Machern von German-OCR.

Benchmark für die Extraktion von Rechnungen, Gutschriften und Kleinbetragsbelegen aus der Schweiz, Deutschland und Österreich — 100 % synthetisch, mit vollständiger Ground Truth, prüfsummen-korrekten Kennungen (IBAN, QR-Referenz, RF, CHE-UID, USt-IdNr., ATU) und echten, scannbaren QR-Codes (Swiss QR bzw. EPC-QR/GiroCode).

Datensatz: huggingface.co/datasets/keyvan-ai/belegbench — Configs ch, de, at (v2, 3 × 3 334 = 10 002 Belege) und ch-v1 (300 Belege, v1-Referenz).

Warum

Bisher gab es kein öffentliches Benchmark für Belege mit den Spezifika aller drei Länder — QR-IBAN und QR-Referenz (CH), § 14 UStG-Pflichtangaben, Steuernummer-Landesformate und § 13b (DE), § 11 UStG, UID-Pflicht über 10 000 € und 13 %-Satz (AT) — und vor allem keines, das misst, was für Treuhänder und Steuerberater zählt: die Halluzinationsrate. Die Ground Truth enthält nur, was auf dem Beleg gedruckt ist; jedes „ergänzte" Feld ist eine Halluzination.

Benchmark laufen lassen

pip install belegbench
# HF-Datensatz, Config de, 300 Belege, Belegant-Pipeline (Schema + QR-Oracle + Verifier)
python -m belegbench.run_eval --model belegant-4b --base-url http://localhost:11434/v1 --config de --n 300 --mode verified
# rohes Basismodell, Config ch
python -m belegbench.run_eval --model qwen3.5:4b --config ch --n 300 --mode raw

Modi: raw (Prompt + freies JSON) · schema (Constrained Decoding mit striktem JSON-Schema) · verified (Schema + QR-Oracle-Merge + deterministischer Verifier = Belegant-Pipeline).

Metriken (belegbench/metrics.py): Field-Level Exact Match, Halluzinationsrate, Verify-Catch-Rate, Parse-Fehler, Latenz — gesamt und je Feld. Ergebnisse landen als JSON in belegbench/results/.

Ergebnisse

v2 (CH / DE / AT, je n = 100, Modus verified)

Modell Modus Config n Field-EM Halluzinationsrate Parse-Fehler
German-OCR-3-8 verified ch 97 95.8 % 6.7 % 0/97
German-OCR-3-8 verified de 100 95.5 % 4.7 % 0/100
German-OCR-3-8 verified at 100 95.3 % 5.0 % 0/100

Je Feld (Field-EM, Auszug):

Feld AT CH DE
invoice_number 99 % 99 % 99 %
invoice_date 99 % 99 % 99 %
total 98 % 98 % 100 %
creditor_iban 98 % 98 % 97 %
supplier_vat_uid 93 % 93 % 92 %
due_date 81 % 77 % 81 %
line_item_count 100 % 99 % 100 %
vat_rates 98 % 96 % 99 %
payment_reference 96 % – 98 %
customer_number 96 % 91 % 94 %

v1 (CH, n = 300, fairer 4B-Vergleich — Config ch-v1)

Modell Field-EM Halluzinationsrate Parse-Fehler
Belegant-4B 84.7 % 0.0 % 3/300
Qwen3.5-4B (roh) 75.8 % 1.2 % 15/100
gemma3:4b (roh) 16.5 % 15.9 % 76/100

Trilingual: FR 81.4 %, IT 83.0 %. Viral-Grafik: belegbench/viral_table.html.

Paket

  • belegbench/schema.py — Extraktionsschema (35 Felder, pydantic) + striktes JSON-Schema für Constrained Decoding
  • belegbench/checks.py — deterministische Prüfungen (IBAN mod-97, QRR, RF/ISO 11649, CHE-UID, DE USt-IdNr., ATU), QR-Oracle-Parser (SPC/EPC), Verifier
  • belegbench/metrics.py — Field-EM / Halluzinationsrate / Verify-Catch-Rate
  • belegbench/run_eval.py — Eval-Harness (OpenAI-kompatible Endpoints, Ollama, vLLM, llama.cpp)

Lizenz

Apache-2.0 · von den Machern von German-OCR

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