HSCodeClassify is a small, on-device logistics 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 (20): 03 Fish & seafood, 08 Edible fruit & nuts, 09 Coffee, tea & spices, 22 Beverages & spirits, 30 Pharmaceuticals, 39 Plastics & articles, 40 Rubber & articles, 44 Wood & articles, 48 Paper & paperboard, 61 Apparel, knitted, 62 Apparel, not knitted, 64 Footwear, 72 Iron & steel, 73 Articles of iron/steel, 84 Machinery & mechanical, 85 Electrical machinery, 87 Vehicles & parts, 90 Optical/medical instr., 94 Furniture & bedding, 95 Toys, games & sports
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/hscodeclassify")
model = AutoModelForSequenceClassification.from_pretrained("flowxai/hscodeclassify")

License & attribution

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