Sarden1: Multilingual PII Detection & Redaction Model

Model Description

Sarden1 is a high-performance token classification model built from scratch for personally identifiable information (PII) detection and redaction. It identifies and labels sensitive entity spans in text across 15 locales, making it suitable for GDPR/HIPAA compliance pipelines, log scrubbing, and document redaction at production scale.

• Developed by: Surpem
• Model Type: Token Classifier (BIO tagging)
• Architecture: Custom Decoder-style Transformer
• Base Model: Trained from scratch — no pretrained base
• License: Apache 2.0
• Languages: en, de, fr, it, es, nl, pt, pl, cs, da, fi, sv (+ en_GB, en_CA, en_AU)

Architecture

Component	Detail
Parameters	~300M
Layers	18 transformer layers
Hidden size	1024
Attention	Grouped Query Attention (16Q / 4KV heads)
FFN	SwiGLU (2730 intermediate)
Positional encoding	RoPE (θ = 500,000)
Normalisation	RMSNorm (no bias)
Tokeniser	GPT-2 BPE (vocab 50,257)
Precision	bfloat16

Entity Types

Sarden1 detects 12 PII entity types using BIO span labelling:

Category	Entity Types
Identity	PERSON, USERNAME, DATE
Contact	EMAIL, PHONE, ADDRESS
Financial	CREDITCARD, SSN
Documents	PASSPORT, DRIVERSLICENSE
Technical	IP
Organisational	ORG

Get Started

import json, torch
from safetensors.torch import load_file
from transformers import AutoTokenizer

# Load weights and config
sd       = load_file("model.safetensors")
cfg      = json.load(open("config.json"))
id2label = {int(k): v for k, v in cfg["id2label"].items()}

# Load tokeniser
tok = AutoTokenizer.from_pretrained(".")

# (Rebuild model from architecture, then:)
model.load_state_dict(sd)
model.eval()

# Inference
text    = "Hi, I'm Jane Smith. Reach me at jane@example.com or 555-1234."
enc     = tok(text, return_offsets_mapping=True, return_tensors="pt")
with torch.no_grad():
    logits = model(enc["input_ids"])["logits"]

preds   = logits.argmax(-1)[0].tolist()
offsets = enc["offset_mapping"][0].tolist()

for pred, (cs, ce) in zip(preds, offsets):
    if cs != ce and id2label.get(pred, "O") != "O":
        print(f"{id2label[pred]:<20} {repr(text[cs:ce])}")

Example output:

PERSON               'Jane Smith'
EMAIL                'jane@example.com'
PHONE                '555-1234'

Citation

@misc{surpem2026sarden1,
      title  = {Sarden1-300M: Multilingual PII Detection \& Redaction Model},
      author = {Surpem},
      year   = {2026},
      url    = {https://huggingface.co/surpem/sarden1-300m},
}