PrivacyFilter

PrivacyFilter is a small, on-device cross text classifier from the FlowX OpenNER family. Developed by FlowX.AI. Runs 100% on-premise / air-gapped, so no data leaves your boundary.

What it does

• Task: text-classification
• Base model: FacebookAI/xlm-roberta-base
• Classes (4): NONE, PERSONAL, FINANCIAL, HEALTH
• Held-out F1: 1.0000
• Runtime: CPU, Apple Silicon, one GPU, or browser/edge via ONNX (INT8). ~100-160 ms/doc on CPU.

Why a small model

Fine-tuned encoders match or beat frontier LLMs on structured, convention-bound extraction, at a fraction of the latency and cost, with zero data egress. Identifiers are validated by checksum (IBAN mod-97, card Luhn, ISIN/LEI, container ISO-6346, VIN, national IDs), a correctness guarantee general LLMs lack. See the FlowX OpenNER benchmark for measured results.

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
tok = AutoTokenizer.from_pretrained("flowxai/privacyfilter")
model = AutoModelForTokenClassification.from_pretrained("flowxai/privacyfilter")

License & attribution

Licensed under the Apache License 2.0. Copyright 2026 FlowX.AI (https://flowx.ai). See the NOTICE file. Trained on synthetic, checksum-validated data.

Part of the FlowX OpenNER model family. Synthetic-data F1 reflects an in-distribution synthetic distribution; validate on real documents before production use.