---
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
base_model: answerdotai/ModernBERT-base
tags:
  - ner
  - on-device
  - privacy
  - flowx
  - openner
  - banking
  - de-identification
  - token-classification
metrics:
  - f1
---

# SanctionScreen

**SanctionScreen** is a small, on-device banking NER model 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:** token-classification
- **Base model:** `answerdotai/ModernBERT-base`
- **Entity types (4):** JURISDICTION, PARTY, SANCTIONED, UBO
- **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
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tok = AutoTokenizer.from_pretrained("flowxai/sanctionscreen")
model = AutoModelForTokenClassification.from_pretrained("flowxai/sanctionscreen")
```

## 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._