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--- |
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datasets: |
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- ai4privacy/pii-masking-400k |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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base_model: |
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- answerdotai/ModernBERT-base |
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pipeline_tag: token-classification |
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tags: |
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- pii |
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- privacy |
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- personal |
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- identification |
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--- |
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# 🐟 PII-RANHA: Privacy-Preserving Token Classification Model |
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## Overview |
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PII-RANHA is a fine-tuned token classification model based on **ModernBERT-base** from Answer.AI. It is designed to identify and classify Personally Identifiable Information (PII) in text data. The model is trained on the `ai4privacy/pii-masking-400k` dataset and can detect 17 different PII categories, such as account numbers, credit card numbers, email addresses, and more. |
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This model is intended for privacy-preserving applications, such as data anonymization, redaction, or compliance with data protection regulations. |
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## Model Details |
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### Model Architecture |
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- **Base Model**: `answerdotai/ModernBERT-base` |
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- **Task**: Token Classification |
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- **Number of Labels**: 18 (17 PII categories + "O" for non-PII tokens) |
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## Usage |
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### Installation |
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To use the model, ensure you have the `transformers` and `datasets` libraries installed: |
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```bash |
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pip install transformers datasets |
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``` |
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Inference Example |
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Here’s how to load and use the model for PII detection: |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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# Load the model and tokenizer |
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model_name = "scampion/piiranha" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForTokenClassification.from_pretrained(model_name) |
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# Create a token classification pipeline |
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pii_pipeline = pipeline("token-classification", model=model, tokenizer=tokenizer) |
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# Example input |
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text = "My email is [email protected] and my phone number is 555-123-4567." |
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# Detect PII |
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results = pii_pipeline(text) |
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for entity in results: |
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print(f"Entity: {entity['word']}, Label: {entity['entity']}, Score: {entity['score']:.4f}") |
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``` |
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```bash |
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Entity: Ġj, Label: I-ACCOUNTNUM, Score: 0.6445 |
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Entity: ohn, Label: I-ACCOUNTNUM, Score: 0.3657 |
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Entity: ., Label: I-USERNAME, Score: 0.5871 |
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Entity: do, Label: I-USERNAME, Score: 0.5350 |
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Entity: Ġ555, Label: I-ACCOUNTNUM, Score: 0.8399 |
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Entity: -, Label: I-SOCIALNUM, Score: 0.5948 |
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Entity: 123, Label: I-SOCIALNUM, Score: 0.6309 |
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Entity: -, Label: I-SOCIALNUM, Score: 0.6151 |
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Entity: 45, Label: I-SOCIALNUM, Score: 0.3742 |
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Entity: 67, Label: I-TELEPHONENUM, Score: 0.3440 |
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``` |
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## Training Details |
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### Dataset |
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The model was trained on the ai4privacy/pii-masking-400k dataset, which contains 400,000 examples of text with annotated PII tokens. |
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### Training Configuration |
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- **Batch Size:** 32 |
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- **Learning Rate:** 5e-5 |
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- **Epochs:** 4 |
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- **Optimizer:** AdamW |
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- **Weight Decay:** 0.01 |
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- **Scheduler:** Linear learning rate scheduler |
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### Evaluation Metrics |
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The model was evaluated using the following metrics: |
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- Precision |
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- Recall |
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- F1 Score |
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- Accuracy |
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| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|-------|---------------|-----------------|-----------|--------|-------|----------| |
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| 1 | 0.017100 | 0.017944 | 0.897562 | 0.905612 | 0.901569 | 0.993549 | |
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| 2 | 0.011300 | 0.014114 | 0.915451 | 0.923319 | 0.919368 | 0.994782 | |
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| 3 | 0.005000 | 0.015703 | 0.919432 | 0.928394 | 0.923892 | 0.995136 | |
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| 4 | 0.001000 | 0.022899 | 0.921234 | 0.927212 | 0.924213 | 0.995267 | |
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Would you like me to help analyze any trends in these metrics? |
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## License |
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This model is licensed under the Commons Clause Apache License 2.0. For more details, see the Commons Clause website. |
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For another license, contact the author. |
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## Author |
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Name: Sébastien Campion |
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Email: [email protected] |
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Date: 2025-01-30 |
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Version: 0.1 |
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## Citation |
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If you use this model in your work, please cite it as follows: |
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```bibtex |
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@misc{piiranha2025, |
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author = {Sébastien Campion}, |
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title = {PII-RANHA: A Privacy-Preserving Token Classification Model}, |
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year = {2025}, |
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version = {0.1}, |
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url = {https://huggingface.co/sebastien-campion/piiranha}, |
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} |
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``` |
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## Disclaimer |
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This model is provided "as-is" without any guarantees of performance or suitability for specific use cases. |
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Always evaluate the model's performance in your specific context before deployment. |
