---
title: Multilingual PII Detection
emoji: 🌍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
---

# Multilingual PII Detection with BERT

This Space demonstrates a **multilingual BERT model** fine-tuned for detecting Personal Identifiable Information (PII) in text across multiple languages.

## Model Details

- **Base Model**: [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased)
- **Task**: Token Classification / Named Entity Recognition (NER)
- **Number of Entity Types**: 39
- **Languages**: Supports 100+ languages including English, Spanish, French, German, Chinese, Arabic, and more

## Detectable PII Types

The model can identify 39 different types of personal information:

### Identity Information
- NAME, USERNAME, PREFIX, GENDER, AGE, JOB, BLOODTYPE

### Contact Information
- EMAIL, PHONENUMBER, PHONEIMEI, STREET, ADDRESS, ZIPCODE, GEO, NEARBYGPSCOORDINATE

### Financial Information
- CREDITCARDNUMBER, CREDITCARDISSUER, IBAN, BIC, ACCOUNTNAME, CURRENCY, COINADDRESS

### Government IDs
- SSN (Social Security Number)

### Vehicle Information
- VEHICLEVIN (Vehicle Identification Number)
- VEHICLEVRM (Vehicle Registration Mark)

### Technical Information
- IP, MAC, URL, PASSWORD

### Organization
- ORG

### Temporal Information
- DATE, TIME

### Physical Attributes
- HEIGHT, WEIGHTS, COLOR

### Other
- NUM, ORDINALDIRECTION, MISC

## How It Works

1. **Input**: User provides text that may contain personal information
2. **Tokenization**: Text is split into tokens using BERT tokenizer
3. **Classification**: Each token is classified into one of 27 entity types or "O" (no entity)
4. **Visualization**: Detected entities are highlighted with different colors

## Training Details

- Learning Rate: 5e-05
- Batch Size: 16 (train), 64 (eval)
- Epochs: 3
- Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-08)
- Warmup Steps: 500

## Use Cases

- **Data Privacy**: Identify PII before sharing documents
- **Data Anonymization**: Find information that needs masking
- **Compliance**: Help meet GDPR, CCPA requirements
- **Security**: Detect sensitive information leaks

## Limitations

- Maximum input length: 512 tokens
- Optimized for English text
- May not detect all variations of PII
- Performance depends on text format and quality

## Example Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification

model_name = "your-username/your-space-name"  # Update after deployment
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

text = "My name is John Smith and my email is john@example.com"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```

## License

Apache 2.0