File size: 1,830 Bytes
543f923 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ---
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: answerdotai/ModernBERT-base
tags:
- ner
- on-device
- privacy
- flowx
- openner
- healthcare
- de-identification
- text-classification
metrics:
- f1
---
# IntentRouter
**IntentRouter** is a small, on-device healthcare 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:** `answerdotai/ModernBERT-base`
- **Classes (8):** REFILL_REQUEST, NEW_PRESCRIPTION, STOCK_ORDER, RETURNS_REBATE, DELIVERY_ISSUE, BILLING_QUERY, CLINICAL_URGENT, GENERAL_ADMIN
- **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, AutoModelForSequenceClassification
tok = AutoTokenizer.from_pretrained("flowxai/intentrouter")
model = AutoModelForSequenceClassification.from_pretrained("flowxai/intentrouter")
```
## 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._ |