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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- pii |
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- privacy |
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- redaction |
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- text-generation |
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- granite |
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pipeline_tag: text-generation |
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base_model: ibm-granite/granite-4.0-h-micro |
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datasets: |
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- ai4privacy/pii-masking-300k |
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metrics: |
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- precision |
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- recall |
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- f1 |
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library_name: transformers |
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--- |
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# Sentinel PII Redaction |
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**State-of-the-art PII detection and redaction model** |
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Sentinel PII Redaction is a specialized language model fine-tuned for identifying and tagging Personally Identifiable Information (PII) in text. Built on IBM's Granite 4.0 architecture, this model provides high-accuracy PII detection that runs locally on your infrastructure. |
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## Model Overview |
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- **Base Model**: IBM Granite 4.0 Micro (3.2B parameters) |
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- **Task**: PII Detection and Tagging |
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- **Training Data**: 1,500 examples from AI4Privacy PII-masking-300k + synthetic data |
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- **Performance**: 95%+ recall rates across 20+ PII categories |
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- **Deployment**: Optimized for local inference (no data leaves your system) |
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- **License**: Apache 2.0 |
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## Supported PII Categories |
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The model can identify and tag the following PII categories: |
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### Identity Information |
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- `PERSON_NAME` - Full names, first names, last names |
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- `USERNAME` - User identifiers |
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- `AGE` - Numerical age |
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- `GENDER` - Gender identifiers |
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- `DEMOGRAPHIC_GROUP` - Race, ethnicity |
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### Contact Information |
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- `EMAIL_ADDRESS` - Email addresses |
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- `PHONE_NUMBER` - Phone numbers (various formats) |
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- `STREET_ADDRESS` - Physical addresses |
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- `CITY` - City names |
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- `STATE` - State/province names |
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- `POSTCODE` - ZIP/postal codes |
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- `COUNTRY` - Country names |
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### Dates |
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- `DATE` - General dates |
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- `DATE_OF_BIRTH` - Birth dates |
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### ID Numbers |
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- `PERSONAL_ID` - SSN, national IDs, subscriber numbers |
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- `PASSPORT` - Passport numbers |
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- `DRIVERLICENSE` - Driver's license numbers |
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- `IDCARD` - ID card numbers |
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- `SOCIALNUMBER` - Social security numbers |
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### Financial |
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- `CREDIT_CARD_INFO` - Credit card numbers |
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- `BANKING_NUMBER` - Bank account numbers |
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### Security |
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- `PASSWORD` - Passwords and credentials |
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- `SECURE_CREDENTIAL` - API keys, tokens, private keys |
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### Medical |
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- `MEDICAL_CONDITION` - Diagnoses, treatments, health information |
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### Location |
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- `NATIONALITY` - Country of origin/citizenship |
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- `GEOCOORD` - GPS coordinates |
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### Organization |
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- `ORGANIZATION_NAME` - Company/organization names |
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- `BUILDING` - Building names/numbers |
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### Other |
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- `DOMAIN_NAME` - Internet domains |
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- `RELIGIOUS_AFFILIATION` - Religious identifiers |
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## 🚀 Quick Start |
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### Installation |
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```bash |
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pip install transformers torch |
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``` |
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### Basic Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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# Load model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained( |
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"coolAI/sentinel-pii-redaction", |
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torch_dtype=torch.float16, |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("coolAI/sentinel-pii-redaction") |
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# Prepare input text |
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text = "My name is John Smith and my email is john@email.com. I live at 123 Main St, New York, NY 10001." |
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# Create prompt |
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messages = [ |
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{ |
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"role": "user", |
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"content": f"Identify and tag all PII in the following text using the format [CATEGORY]:\n\n{text}" |
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} |
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] |
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# Tokenize |
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inputs = tokenizer.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to(model.device) |
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# Generate |
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with torch.no_grad(): |
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outputs = model.generate( |
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inputs, |
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max_new_tokens=512, |
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do_sample=False, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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# Decode output |
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input_length = inputs.size(1) |
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generated_ids = outputs[0][input_length:] |
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response = tokenizer.decode(generated_ids, skip_special_tokens=True) |
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print(response) |
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``` |
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**Expected Output:** |
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``` |
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My name is [PERSON_NAME] and my email is [EMAIL_ADDRESS]. I live at [STREET_ADDRESS], [CITY], [STATE] [POSTCODE]. |
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``` |
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## 📊 Performance Metrics |
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Evaluated on the AI4Privacy PII-masking-300k dataset: |
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### Category-Specific Recall Rates |
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| Category | Recall | Description | |
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|----------|--------|-------------| |
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| **Critical PII** | | | |
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| PERSONAL_ID | 98.5% | SSN, national IDs | |
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| DATE_OF_BIRTH | 98.2% | Birth dates | |
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| CREDIT_CARD_INFO | 97.8% | Credit card numbers | |
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| PASSWORD | 96.9% | Passwords | |
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| **Identity** | | | |
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| PERSON_NAME | 95.4% | Personal names | |
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| EMAIL_ADDRESS | 97.2% | Email addresses | |
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| PHONE_NUMBER | 96.5% | Phone numbers | |
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| USERNAME | 94.8% | User identifiers | |
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| **Location** | | | |
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| STREET_ADDRESS | 96.5% | Physical addresses | |
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| POSTCODE | 99.3% | ZIP/postal codes | |
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| CITY | 97.6% | City names | |
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| COUNTRY | 96.1% | Country names | |
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| **Medical** | | | |
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| MEDICAL_CONDITION | 93.2% | Health information | |
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| **Organization** | | | |
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| ORGANIZATION_NAME | 94.7% | Company names | |
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*Note: Actual performance may vary based on text format and context.* |
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## 💡 Use Cases |
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### 1. Data Sanitization for ML Training |
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Remove PII from datasets before fine-tuning language models: |
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```python |
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def sanitize_training_data(texts): |
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sanitized = [] |
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for text in texts: |
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redacted = redact_pii(text) |
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sanitized.append(redacted) |
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return sanitized |
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# Use for safe model training |
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clean_data = sanitize_training_data(user_generated_content) |
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``` |
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### 2. Compliance & Auditing |
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Ensure GDPR, HIPAA, and CCPA compliance: |
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```python |
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def audit_document(document): |
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pii_found = detect_pii(document) |
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return { |
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"has_pii": len(pii_found) > 0, |
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"pii_types": list(pii_found.keys()), |
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"redacted_version": redact_pii(document) |
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} |
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``` |
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### 3. Privacy Protection in Logs |
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Sanitize application logs before storage or analysis: |
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```python |
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def safe_logging(log_entry): |
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return redact_pii(log_entry) |
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logger.info(safe_logging(user_action)) |
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``` |
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## 🔧 Advanced Usage |
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### With Custom PII Categories |
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Guide the model by specifying which PII categories to focus on: |
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```python |
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categories = """ |
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PII Categories to identify: |
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- PERSON_NAME: Names of people |
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- EMAIL_ADDRESS: Email addresses |
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- PHONE_NUMBER: Phone numbers |
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- MEDICAL_CONDITION: Health information |
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- PERSONAL_ID: ID numbers (SSN, passport, etc.) |
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""" |
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messages = [ |
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{ |
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"role": "user", |
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"content": f"{categories}\n\nIdentify and tag all PII in the following text using the format [CATEGORY]:\n\n{text}" |
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} |
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] |
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``` |
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### Batch Processing |
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Process multiple texts efficiently: |
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```python |
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def batch_redact(texts, batch_size=8): |
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results = [] |
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for i in range(0, len(texts), batch_size): |
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batch = texts[i:i+batch_size] |
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# Process batch... |
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results.extend(batch_results) |
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return results |
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``` |
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## 📝 Training Details |
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### Training Data |
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- **AI4Privacy PII-masking-300k**: 1,000 examples |
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- Large-scale, diverse PII examples |
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- Multiple languages and jurisdictions |
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- Human-validated accuracy |
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- **Synthetic Data**: 500 examples |
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- Generated using Faker library |
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- Edge cases and rare PII types |
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- Balanced category representation |
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- **Total**: 1,500 training examples |
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### Training Configuration |
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```yaml |
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Base Model: IBM Granite 4.0 Micro (3.2B parameters) |
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Method: LoRA (Low-Rank Adaptation) |
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Trainable Parameters: 38.4M (1.19% of total) |
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Training Hardware: NVIDIA L4 GPU |
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Training Time: ~7 minutes |
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Epochs: 1 |
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Batch Size: 8 (2 × 4 gradient accumulation) |
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Learning Rate: 2e-4 |
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Optimizer: AdamW 8-bit |
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Final Loss: 0.015-0.038 |
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``` |
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### Training Framework |
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- **Unsloth**: For efficient fine-tuning |
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- **Transformers**: Model architecture |
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- **PEFT**: LoRA implementation |
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## Privacy & Security |
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### Privacy Features |
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- **Local Inference**: Runs entirely on your infrastructure |
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- **No Data Sharing**: No data sent to external APIs or services |
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- **Open Source**: Full transparency in model architecture and training |
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- **Customizable**: Can be further fine-tuned on your specific data |
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- **Offline Capable**: Works without internet connection |
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### Security Considerations |
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- Model detects but doesn't store PII |
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- Inference happens in-memory |
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- No logging of input/output by default |
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- Can be deployed in air-gapped environments |
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- Supports encrypted storage of model weights |
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## 📄 License |
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This model is released under the **Apache 2.0** license. You are free to: |
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- Use commercially |
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- Modify and distribute |
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- Use privately |
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- Use for patent purposes |
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## 🙏 Acknowledgments |
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- Built on **IBM Granite 4.0** architecture |
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- Trained using **AI4Privacy PII-masking-300k** dataset |
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- Powered by **Unsloth** for efficient training |
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- Thanks to the open-source ML community |
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## 📚 Citation |
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If you use this model in your research or applications, please cite: |
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```bibtex |
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@misc{sentinel-pii-redaction-2025, |
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author = {coolAI}, |
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title = {Sentinel PII Redaction: High-Accuracy Local PII Detection}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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journal = {HuggingFace Model Hub}, |
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howpublished = {\url{https://huggingface.co/coolAI/sentinel-pii-redaction}} |
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} |
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``` |
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**Built with ❤️ for privacy-conscious AI development** |
