This model detects 9 classes of sensitive personal and financial information using BIO tagging. It supports multilingual input with strong performance on French, English, and Tunisian Arabic-Latin mixed text.

Detected Entities

Entity	Description	Examples
PER	Person name	Ahmed Ben Salah, Marie Dupont
ORG	Organization	TechCorp Tunisie, Banque de France
LOC	Location	Tunis, Paris, avenue Habib Bourguiba
MISC	Miscellaneous named entity	Visa, MasterCard
IBAN	International Bank Account Number	TN59 1000 6035 1835 9848 3270, FR76 3000...
CARD	Credit / Debit card number	4532 1234 5678 9012
PHONE	Phone number (international formats)	+216 71 234 567, +33 1 23 45 67 89
EMAIL	Email address	ahmed.bensalah@techcorp.tn
DATE	Date (any format)	15/03/2025, 14 mars 2025, 12/2028

Performance

Global Scores (epoch 4 / 14,944 steps)

Precision	Recall	F1-Score	Accuracy
89.39%	91.47%	90.42%	98.87%

Per-Class F1 (evaluation set)

Entity	F1
PER	100%
ORG	88.9%
LOC	80.0%
IBAN	100%
CARD	100%
PHONE	100%
EMAIL	100%
DATE	66.7%

Training Data

Source	Volume	Classes
Jean-Baptiste/wikiner_fr	120,682 sentences	PER, ORG, LOC, MISC
ai4privacy/pii-masking-200k	82,545 examples	IBAN, CARD, PHONE, EMAIL, DATE
Faker (synthetic)	40,000 examples (8,000 × 5 classes)	IBAN, CARD, PHONE, EMAIL, DATE
TOTAL	239,099 train · 17,538 val	9 classes

Quick Start

With Optimum (recommended for ONNX)

python

from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline

model = ORTModelForTokenClassification.from_pretrained(
    "yalen-ai/distilbert_pii_ner_yalen",
    file_name="model_quantized.onnx"
)
tokenizer = AutoTokenizer.from_pretrained("yalen-ai/distilbert_pii_ner_yalen")

ner = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Contact: ahmed.bensalah@techcorp.tn | Tel: +216 71 234 567 | IBAN: TN59 1000 6035 1835 9848 3270"
results = ner(text)

for entity in results:
    print(f"[{entity['entity_group']}] '{entity['word']}' (score: {entity['score']:.3f})")

Output:

[EMAIL] 'ahmed.bensalah@techcorp.tn' (score: 0.998)
[PHONE] '+216 71 234 567' (score: 0.984)
[IBAN]  'TN59 1000 6035 1835 9848 3270' (score: 0.757)

With ONNX Runtime directly

python

import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np
import json

# Load tokenizer and label map
tokenizer = AutoTokenizer.from_pretrained("yalen-ai/distilbert_pii_ner_yalen")
with open("config.json") as f:
    cfg = json.load(f)
id2label = cfg["id2label"]

# Load ONNX session
session = ort.InferenceSession("model_quantized.onnx", providers=["CPUExecutionProvider"])

def predict(text):
    inputs = tokenizer(text, return_tensors="np", truncation=True, max_length=512)
    outputs = session.run(None, dict(inputs))
    logits = outputs[0][0]
    token_ids = inputs["input_ids"][0]
    tokens = tokenizer.convert_ids_to_tokens(token_ids)
    labels = [id2label[str(np.argmax(l))] for l in logits]
    return [(tok, lbl) for tok, lbl in zip(tokens, labels) if lbl != "O" and not tok.startswith("[")]

results = predict("Ahmed Ben Salah travaille chez TechCorp, IBAN: TN59 1000 6035 1835 9848 3270")
for token, label in results:
    print(f"  {token:<30} {label}")

Installation

bash

# For Optimum (ONNX Runtime)
pip install optimum[onnxruntime] transformers

# For direct ONNX Runtime usage
pip install onnxruntime transformers

Model Architecture

Parameter	Value
Base model	distilbert-base-multilingual-cased
Architecture	DistilBertForTokenClassification
Hidden size	768
Attention heads	12
Hidden layers	6
Max tokens	512
Vocab size	119,547
Labels	19 (O + 9×BIO)
Format	ONNX INT8 (avx2 quantization)

Model Size Comparison

Stage	Format	Size
Fine-tuning	PyTorch FP32	543 MB
Export	ONNX FP32	539 MB
Quantization	ONNX INT8 avx2	139 MB

Label Map

json

{
  "0":  "O",
  "1":  "B-PER",   "2":  "I-PER",
  "3":  "B-ORG",   "4":  "I-ORG",
  "5":  "B-LOC",   "6":  "I-LOC",
  "7":  "B-MISC",  "8":  "I-MISC",
  "9":  "B-IBAN",  "10": "I-IBAN",
  "11": "B-CARD",  "12": "I-CARD",
  "13": "B-PHONE", "14": "I-PHONE",
  "15": "B-EMAIL", "16": "I-EMAIL",
  "17": "B-DATE",  "18": "I-DATE"
}

Intended Use

Privacy compliance (GDPR, Tunisian Data Protection Law)
Document redaction — anonymize sensitive documents before sharing
Data loss prevention (DLP) — detect accidental PII leaks in logs or messages
Financial document processing — extract IBAN/card numbers for validation
Healthcare & insurance — detect names, dates and contact information
Edge deployment — ONNX INT8 runs efficiently on CPU without GPU

Limitations

Maximum input length: 512 tokens (long documents should be split by sentence or paragraph)
DATE detection is the weakest class (F1 ~67%) — dates in full French text ("14 mars 2025") are harder to detect than numeric formats
Card number detection works best with standard spacing (XXXX XXXX XXXX XXXX)
MISC class is inherited from WikiNER and may catch general named entities beyond PII

About Yalen AI

Yalen Sentinel Pulse is an AI-powered platform for PII detection and data privacy protection, developed by the Yalen AI team. It combines regex patterns, ML models, and NER to provide comprehensive sensitive data identification.

Platform: Yalen Sentinel Pulse
Contact: hello@yalen.ai

License

MIT — free for commercial and research use.