IbanDetect

IbanDetect 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): ACCOUNT, IBAN, ROUTING, SWIFT
• Trained on: synthetic banking payment records.
• Runtime: CPU, Apple Silicon, one GPU, or browser/edge via ONNX (INT8). ~100-160 ms/doc on CPU.

Evaluation

Held-out F1 on synthetic data: 0.9488. Train and test are drawn from the same generator, so this describes performance on that generator's distribution rather than on your documents.

It is one of only two models in this family not reporting exactly 1.0000, which is worth reading in both directions. It is a weaker number than its siblings publish, and a more informative one: a 1.0 elsewhere in this family reflects a generated task that was trivially separable rather than a better model.

There is no per-label breakdown, only this aggregate, so it cannot tell you which labels carry the score. Validate on your own documents before production use.

What this model does not do

It labels spans. It does not validate them, and nothing in this repository does: no check digit is verified anywhere here, not IBAN mod-97, not the Luhn algorithm, not ISIN, LEI, VIN or ISO-6346. A span this model labels as an identifier may not be a valid identifier. Validate downstream if your use needs that guarantee.

Correction, 2026-09-14. Until this date the card claimed that identifiers are "validated by checksum (IBAN mod-97, card Luhn, ISIN/LEI, container ISO-6346, VIN, national IDs), a correctness guarantee general LLMs lack". That sentence was shared boilerplate across the OpenNER family and it was not true of any model in it. These repositories contain a config, weights, an ONNX export, a tokenizer and a metrics file, and no validation code of any kind. If you relied on that sentence, the guarantee it described does not exist and never did.

The licence note at the foot of this card says the model was trained on "synthetic, checksum-validated data". That is a statement about how the training corpus was generated. It is not a statement about anything this model checks when you run it, and the two were being read as one claim.

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.

Usage

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

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.