Piiranha

🚀 PII-RANHA: Privacy-Preserving Token Classification Model

PII-RANHA is a fine - tuned token classification model based on ModernBERT - base from Answer.AI. It aims to identify and classify Personally Identifiable Information (PII) in text data. Trained on the ai4privacy/pii - masking - 400k dataset, it can detect 17 different PII categories, including account numbers, credit card numbers, email addresses, etc. This model is designed for privacy - preserving applications like data anonymization, redaction, or compliance with data protection regulations.

✨ Features

• Fine - tuned on the ai4privacy/pii - masking - 400k dataset.
• Capable of detecting 17 different PII categories.
• Suitable for various privacy - preserving applications.

📦 Installation

To use the model, make sure you have the transformers and datasets libraries installed:

pip install transformers datasets

💻 Usage Examples

Basic Usage

Here’s how to load and use the model for PII detection:

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

# Load the model and tokenizer
model_name = "scampion/piiranha"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Create a token classification pipeline
pii_pipeline = pipeline("token - classification", model=model, tokenizer=tokenizer)

# Example input
text = "My email is john.doe@example.com and my phone number is 555 - 123 - 4567."

# Detect PII
results = pii_pipeline(text)
for entity in results:
    print(f"Entity: {entity['word']}, Label: {entity['entity']}, Score: {entity['score']:.4f}")

Sample Output

Entity: Ġj, Label: I - ACCOUNTNUM, Score: 0.6445
Entity: ohn, Label: I - ACCOUNTNUM, Score: 0.3657
Entity: ., Label: I - USERNAME, Score: 0.5871
Entity: do, Label: I - USERNAME, Score: 0.5350
Entity: Ġ555, Label: I - ACCOUNTNUM, Score: 0.8399
Entity: -, Label: I - SOCIALNUM, Score: 0.5948
Entity: 123, Label: I - SOCIALNUM, Score: 0.6309
Entity: -, Label: I - SOCIALNUM, Score: 0.6151
Entity: 45, Label: I - SOCIALNUM, Score: 0.3742
Entity: 67, Label: I - TELEPHONENUM, Score: 0.3440

📚 Documentation

Model Details

Training Details

Dataset

The model was trained on the ai4privacy/pii - masking - 400k dataset, which contains 400,000 examples of text with annotated PII tokens.

Training Configuration

• Batch Size: 32
• Learning Rate: 5e - 5
• Epochs: 4
• Optimizer: AdamW
• Weight Decay: 0.01
• Scheduler: Linear learning rate scheduler

Evaluation Metrics

The model was evaluated using the following metrics:

• Precision
• Recall
• F1 Score
• Accuracy

📄 License

This model is licensed under the Commons Clause Apache License 2.0. For more details, see the Commons Clause website. For another license, contact the author.

Author

• Name: Sébastien Campion
• Email: sebastien.campion@foss4.eu
• Date: 2025 - 01 - 30
• Version: 0.1

Citation

If you use this model in your work, please cite it as follows:

@misc{piiranha2025,
  author = {Sébastien Campion},
  title = {PII - RANHA: A Privacy - Preserving Token Classification Model},
  year = {2025},
  version = {0.1},
  url = {https://huggingface.co/sebastien - campion/piiranha},
}

Disclaimer

This model is provided "as - is" without any guarantees of performance or suitability for specific use cases. Always evaluate the model's performance in your specific context before deployment.