---
language:
  - fr
  - en
  - ar
  - multilingual
license: mit
tags:
  - distilbert
  - ner
  - pii
  - privacy
  - gdpr
  - onnx
  - quantized
  - token-classification
  - tunisian
pipeline_tag: token-classification
model-index:
  - name: distilbert_pii_ner_yalen
    results:
      - task:
          type: token-classification
        metrics:
          - type: f1
            value: 0.9042
            name: F1 (test set)
            verified: false
---

# distilbert_pii_ner_yalen — PII & NER Detector (9 classes · ONNX INT8)

Fine-tuned and quantized version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) for PII and Named Entity Recognition detection.

Developed by [Yalen AI](https://huggingface.co/yalen-ai) as part of the Yalen Sentinel Pulse privacy protection platform.

> **Model format**: ONNX INT8 quantized — 74% smaller than the original PyTorch model (139 MB vs 543 MB), with minimal accuracy loss.

---

## Model Description

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](https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr) | 120,682 sentences | PER, ORG, LOC, MISC |
| [ai4privacy/pii-masking-200k](https://huggingface.co/datasets/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](mailto:hello@yalen.ai)

---

## License

MIT — free for commercial and research use.