---
title: "Yeco-PII v1.0 · YecoAI Research"
description: "Proprietary 278M PII model beating OpenAI Privacy Filter (1.5B) on all 6 European languages (F1 0.966 vs 0.769)."
url: "https://yecoai.com/en/research/yeco-pii-v1"
locale: "en"
published: "2026-09-10"
updated: "2026-09-29"
category: "Release"
---

# Yeco-PII v1.0

Proprietary 278M model. Beats OpenAI Privacy Filter 1.5B (F1 0.966 vs 0.769) on all 6 languages.

*10 Sep 2026 · Release*

## Abstract

We present Yeco-PII v1.0, a 278M-parameter neural model for PII detection and anonymization that beats the OpenAI Privacy Filter (1.5B) with 5.4× fewer parameters: F1 0.966 vs 0.769, winning all 6 European languages.

## Benchmarks

Evaluated on real-world rows never seen in training, with third-party annotations (Ai4Privacy). Same test for every model. Gaps of 20 to 38 F1 points in our favor on every language.

- 278M parameters · \~1.1 GB, standard HuggingFace format
- 24 protected entity types: names, tax IDs, VAT, IBAN, cards, addresses, cadastral data
- Certified on IT, EN, FR, DE, ES, NL — native Italian
- \~3,700 characters/second on CPU · \~1 GB RAM at scale

## Why size is not capability

Detection is not a brute-force problem. Checksum networks (mod-97 for IBAN, Luhn for cards) only catch what a formula can validate. Names, addresses, organizations and dates have no checksum: real contextual learning is required, and that is exactly where larger competitors lose 20-30 F1 points.

## Conclusion

Compact, rigorously evaluated models can out-engineer giants on vertical tasks. Yeco-PII is proprietary software; full technical documentation is reserved for licensed clients.

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