OpenMed-PII-SuperClinical-Small-44M-v1: Model Card

PII Detection Model | 44M Parameters | Open Source

Model Description

OpenMed-PII-SuperClinical-Small-44M-v1 is a transformer-based token classification model fine-tuned for Personally Identifiable Information (PII) detection in text. This model identifies and classifies 54 types of sensitive information including names, addresses, SSNs, medical record numbers, and more.

Key Features

• High Accuracy: Achieves strong F1 scores across diverse PII categories
• Comprehensive Coverage: Detects 50+ entity types spanning personal, financial, medical, and contact information
• Privacy-Focused: Designed for de-identification and compliance with HIPAA, GDPR, and other privacy regulations
• Production-Ready: Optimized for real-world text processing pipelines

Performance

Evaluated on a stratified 2,000-sample test set from NVIDIA Nemotron-PII:

Top 10 PII Models

Best Performing Entities

Challenging Entities

These entity types have lower performance and may benefit from additional post-processing:

Supported Entity Types

This model detects 54 PII entity types organized into categories:

Identifiers (16 types)

| Entity | Description | |:---|:---| | `account_number` | Account Number | | `api_key` | Api Key | | `bank_routing_number` | Bank Routing Number | | `certificate_license_number` | Certificate License Number | | `credit_debit_card` | Credit Debit Card | | `cvv` | Cvv | | `employee_id` | Employee Id | | `health_plan_beneficiary_number` | Health Plan Beneficiary Number | | `mac_address` | Mac Address | | `medical_record_number` | Medical Record Number | | ... | *and 6 more* |

Personal Info (14 types)

| Entity | Description | |:---|:---| | `age` | Age | | `biometric_identifier` | Biometric Identifier | | `blood_type` | Blood Type | | `date_of_birth` | Date Of Birth | | `education_level` | Education Level | | `first_name` | First Name | | `last_name` | Last Name | | `gender` | Gender | | `language` | Language | | `occupation` | Occupation | | ... | *and 4 more* |

Contact Info (4 types)

| Entity | Description | |:---|:---| | `email` | Email | | `phone_number` | Phone Number | | `fax_number` | Fax Number | | `url` | Url |

Location (6 types)

| Entity | Description | |:---|:---| | `city` | City | | `coordinate` | Coordinate | | `country` | Country | | `county` | County | | `state` | State | | `street_address` | Street Address |

Network Info (3 types)

| Entity | Description | |:---|:---| | `device_identifier` | Device Identifier | | `ipv4` | Ipv4 | | `ipv6` | Ipv6 |

Temporal (3 types)

| Entity | Description | |:---|:---| | `date` | Date | | `date_time` | Date Time | | `time` | Time |

Organization (1 types)

| Entity | Description | |:---|:---| | `company_name` | Company Name |

Usage

Quick Start

from transformers import pipeline

# Load the PII detection pipeline
ner = pipeline("ner", model="openmed/OpenMed-PII-SuperClinical-Small-44M-v1", aggregation_strategy="simple")

text = """
Patient John Smith (DOB: 03/15/1985, SSN: 123-45-6789) was seen today.
Contact: [email protected], Phone: (555) 123-4567.
Address: 456 Oak Street, Boston, MA 02108.
"""

entities = ner(text)
for entity in entities:
    print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")

De-identification Example

def redact_pii(text, entities, placeholder='[REDACTED]'):
    """Replace detected PII with placeholders."""
    # Sort entities by start position (descending) to preserve offsets
    sorted_entities = sorted(entities, key=lambda x: x['start'], reverse=True)
    redacted = text
    for ent in sorted_entities:
        redacted = redacted[:ent['start']] + f"[{ent['entity_group']}]" + redacted[ent['end']:]
    return redacted

# Apply de-identification
redacted_text = redact_pii(text, entities)
print(redacted_text)

Batch Processing

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model_name = "openmed/OpenMed-PII-SuperClinical-Small-44M-v1"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

texts = [
    "Contact Dr. Jane Doe at [email protected]",
    "Patient SSN: 987-65-4321, MRN: 12345678",
]

inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

Training Details

Dataset

• Source: NVIDIA Nemotron-PII
• Format: BIO-tagged token classification
• Labels: 106 total (53 entity types × 2 BIO tags + O)
• Splits: 50K train / 5K validation / 45K test

Training Configuration

• Max Sequence Length: 384 tokens
• Label Strategy: First token only (label_all_tokens=False)
• Framework: Hugging Face Transformers + Trainer API

Intended Use & Limitations

Intended Use

• De-identification: Automated redaction of PII in clinical notes, medical records, and documents
• Compliance: Supporting HIPAA, GDPR, and privacy regulation compliance
• Data Preprocessing: Preparing datasets for research by removing sensitive information
• Audit Support: Identifying PII in document collections

Limitations

Important: This model is intended as an assistive tool, not a replacement for human review.

• False Negatives: Some PII may not be detected; always verify critical applications
• Context Sensitivity: Performance may vary with domain-specific terminology
• Challenging Categories: occupation, time, and sexuality have lower F1 scores
• Language: Primarily trained on English text

Citation

@misc{openmed-pii-2026,
  title = {OpenMed-PII-SuperClinical-Small-44M-v1: PII Detection Model},
  author = {OpenMed Science},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/openmed/OpenMed-PII-SuperClinical-Small-44M-v1}
}

Links

• Organization: OpenMed