xlm_roberta_yalen — PII Detector (IBAN · CARD · PHONE · EMAIL · DATE)

Fine-tuned version of xlm-roberta-base for PII (Personally Identifiable Information) detection with focus on financial and contact data.

Developed by Yalen AI as part of the Yalen Sentinel Pulse privacy protection platform.

---

Model Description

This model performs Named Entity Recognition (NER) to detect sensitive personal and financial information in text. It uses BIO tagging (Begin / Inside / Outside) and supports multilingual input, with strong performance on French, English, and Tunisian Arabic-Latin mixed text.

Detected Entities

Label	Description	Examples
IBAN	International Bank Account Number	TN59 1000 6035 1835 9848 3270, FR76 3000 6000 0112...
CARD	Credit / Debit card number	4532 1234 5678 9012, 5412 7534 2589 6315
PHONE	Phone number (international formats)	+216 23 456 789, 0023 4567890
EMAIL	Email address	support@yalen.tn, ahmed.ben@gmail.com
DATE	Date (any format)	15/03/2025, 14 janvier 1990, 12/2028

---

Quick Start

from transformers import pipeline

ner = pipeline(
    "ner",
    model="yalen-ai/xlm_roberta_yalen",
    aggregation_strategy="simple"
)

text = "Contact: support@yalen.tn | Tel: +216 23 456 789 | 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] 'support@yalen.tn' (score: 1.000)
[PHONE] '+216 23 456 789' (score: 1.000)
[IBAN] 'TN59 1000 6035 1835 9848 3270' (score: 1.000)

---

Installation

pip install transformers torch

---

Usage Examples

Detect all PII in a document

from transformers import pipeline

ner = pipeline("ner", model="yalen-ai/xlm_roberta_yalen", aggregation_strategy="simple")

examples = [
    "Rendez-vous le 15/03/2025, contactez ahmed.ben@gmail.com",
    "Carte bancaire: 4532 1234 5678 9012, expire 12/2028",
    "Virement IBAN FR76 3000 6000 0112 3456 7890 189",
    "Appelez le +216 98 765 432 ou +33 1 23 45 67 89",
]

for text in examples:
    print(f"\nTexte: {text}")
    for e in ner(text):
        print(f"  [{e['entity_group']}] {e['word'].strip('.,;')} ({e['score']:.3f})")

Post-processing tip

The tokenizer may include trailing punctuation in detected spans. Clean it up:

def clean_entity(word: str) -> str:
    return word.strip(".,;:!? ")

results = ner("Email: contact@yalen.tn, Tel: +216 23 000 000")
cleaned = [{**e, "word": clean_entity(e["word"])} for e in results]

With explicit label mapping

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("yalen-ai/xlm_roberta_yalen")
    model = AutoModelForTokenClassification.from_pretrained("yalen-ai/xlm_roberta_yalen")

text = "IBAN: TN59 1000 6035 1835 9848 3270"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

predictions = torch.argmax(outputs.logits, dim=2)
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
labels = [model.config.id2label[p.item()] for p in predictions[0]]

for token, label in zip(tokens, labels):
    if label != "O":
        print(f"{token:20} → {label}")

---

Model Architecture

Parameter	Value
Base model	xlm-roberta-base
Architecture	XLMRobertaForTokenClassification
Hidden size	768
Attention heads	12
Hidden layers	12
Max tokens	512
Vocab size	250,002
Labels	11 (O + 5×BIO)
Model size	~1.06 GB

---

Label Map

{
  "0": "O",
  "1": "B-IBAN",  "2": "I-IBAN",
  "3": "B-CARD",  "4": "I-CARD",
  "5": "B-PHONE", "6": "I-PHONE",
  "7": "B-EMAIL", "8": "I-EMAIL",
  "9": "B-DATE",  "10": "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 dates and contact information

---

Limitations

• Maximum input length: 512 tokens (long documents should be split by sentence or paragraph)
• Trained primarily on French and English data — performance may vary on other languages
• Card number detection works best with standard spacing formats (XXXX XXXX XXXX XXXX)
• Trailing punctuation may be included in token spans — apply post-processing (.strip(".,;"))

---

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.