Open PII models for Arabic: English-first filters are weak on names

English-first PII filters are weak on Arabic names, including standalone first names and kunyas.

I ran into this while redacting text before sending it to an LLM. Off-the-shelf tools did not hold up on Semitic names more generally. On a synthetic set of standalone Hebrew first names (a close stress test for the same class of errors), openai/privacy-filter caught 14% and GLiNER 33%.

I trained Seter, an open (MIT) PII model for Arabic, Hebrew and English. Lenient char-F1 is 0.917 on Arabic, 0.904 on Hebrew and 0.878 on English. On that same first-name set it catches 88%. It runs fully in the browser via ONNX.

Demo: [Seter — Hebrew/Arabic PII redaction - a Hugging Face Space by yinonov](https://huggingface.co/spaces/yinonov/seter-demo)

Model: [shoresh-labs/seter-pii-he-ar · Hugging Face](https://huggingface.co/shoresh-labs/seter-pii-he-ar)

Failure cases from real-world Arabic text are the most useful thing you could send back.