license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
base_model: answerdotai/ModernBERT-base
tags:
- ner
- on-device
- privacy
- flowx
- openner
- logistics
- de-identification
- token-classification
metrics:
- f1
ContainerDetect
ContainerDetect is a small, on-device logistics 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 (3): CONTAINER_NO, SEAL, SIZE_TYPE
- 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/containerdetect")
model = AutoModelForTokenClassification.from_pretrained("flowxai/containerdetect")
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.