FlowX Cross-Industry Models
Collection
On-device cross NER / SLM models. Apache-2.0. FlowX.AI. • 6 items • Updated
Text Classification
Transformers
Safetensors
xlm-roberta
ner
on-device
privacy
flowx
openner
cross
de-identification
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("flowxai/privacyfilter")
model = AutoModelForSequenceClassification.from_pretrained("flowxai/privacyfilter", device_map="auto")Quick Links
PrivacyFilter
PrivacyFilter is a small, on-device cross 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:
FacebookAI/xlm-roberta-base - Classes (4): NONE, PERSONAL, FINANCIAL, HEALTH
- 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/privacyfilter")
model = AutoModelForTokenClassification.from_pretrained("flowxai/privacyfilter")
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.
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Model tree for flowxai/privacyfilter
Base model
FacebookAI/xlm-roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flowxai/privacyfilter")