metadata
language: en
license: apache-2.0
tags:
- token-classification
- ner
- hipaa
- phi
- healthcare
- privacy
- distilbert
datasets:
- custom
pipeline_tag: token-classification
HIPAA PHI Detector (DistilBERT)
A fine-tuned DistilBERT model for detecting Protected Health Information (PHI) in text, covering all 18 HIPAA Safe Harbor categories.
Model Details
- Architecture: DistilBERT (66M params) with token classification head
- Training: Fine-tuned on 5,000+ synthetic HIPAA examples
- Labels: 37 BIO labels (18 entity types x 2 + O)
- Framework: PyTorch / HuggingFace Transformers
Supported Entity Types
| Label | HIPAA Category |
|---|---|
| NAME | Names |
| LOCATION | Geographic subdivisions |
| DATE | Dates |
| PHONE | Phone numbers |
| FAX | Fax numbers |
| Email addresses | |
| SSN | Social Security numbers |
| MRN | Medical record numbers |
| HEALTH_PLAN | Health plan beneficiary numbers |
| ACCOUNT | Account numbers |
| LICENSE | Certificate/license numbers |
| VEHICLE | Vehicle identifiers |
| DEVICE | Device identifiers |
| URL | Web URLs |
| IP | IP addresses |
| BIOMETRIC | Biometric identifiers |
| PHOTO | Photographic images |
| OTHER | Any other unique identifying number |
Usage
from transformers import pipeline
pipe = pipeline("token-classification", model="mkocher/hipaa-phi-detector", aggregation_strategy="simple")
results = pipe("Patient John Smith, SSN 123-45-6789")
Or with the aare-core package:
from aare import HIPAAGuardrail
guardrail = HIPAAGuardrail()
result = guardrail.check("Patient John Smith, SSN 123-45-6789")
if result.blocked:
print(f"PHI detected: {result.violations}")
License
Apache 2.0