OpenMed/OpenMed-PII-LiteClinical-Small-66M-v1

OpenMed-PII-LiteClinical-Base-66M-v1

PII Detection Model | 66M Parameters | Open Source

Model Description

OpenMed-PII-LiteClinical-Base-66M-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:

Metric	Score
Micro F1	0.9485
Precision	0.9554
Recall	0.9418
Macro F1	0.9484
Weighted F1	0.9468
Accuracy	0.9930

Top 10 PII Models

Rank	Model	F1	Precision	Recall
1	OpenMed-PII-SuperClinical-Large-434M-v1	0.9608	0.9685	0.9532
2	OpenMed-PII-BigMed-Large-560M-v1	0.9604	0.9644	0.9565
3	OpenMed-PII-EuroMed-210M-v1	0.9600	0.9681	0.9521
4	OpenMed-PII-SnowflakeMed-568M-v1	0.9594	0.9640	0.9548
5	OpenMed-PII-SuperMedical-Large-355M-v1	0.9592	0.9632	0.9553
6	OpenMed-PII-ClinicalBGE-568M-v1	0.9587	0.9636	0.9538
7	OpenMed-PII-mClinicalE5-Large-560M-v1	0.9582	0.9631	0.9533
8	OpenMed-PII-ModernMed-Large-395M-v1	0.9579	0.9639	0.9520
9	OpenMed-PII-BioClinicalModern-Large-395M-v1	0.9579	0.9656	0.9502
10	OpenMed-PII-ClinicalE5-Large-335M-v1	0.9577	0.9604	0.9550

Best Performing Entities

Entity	F1	Precision	Recall	Support
biometric_identifier	1.000	1.000	1.000	234
credit_debit_card	0.995	1.000	0.991	215
email	0.994	0.995	0.993	763
date_of_birth	0.993	0.986	1.000	273
health_plan_beneficiary_number	0.991	0.982	1.000	216

Challenging Entities

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

Entity	F1	Precision	Recall	Support
pin	0.862	0.872	0.853	136
time	0.859	0.894	0.826	472
gender	0.829	0.806	0.853	190
sexuality	0.824	0.758	0.904	83
occupation	0.647	0.755	0.566	724

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-LiteClinical-Base-66M-v1", aggregation_strategy="simple")

text = """
Patient John Smith (DOB: 03/15/1985, SSN: 123-45-6789) was seen today.
Contact: john.smith@email.com, 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-LiteClinical-Base-66M-v1"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

texts = [
    "Contact Dr. Jane Doe at jane.doe@hospital.org",
    "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-LiteClinical-Base-66M-v1: PII Detection Model},
  author = {OpenMed Science},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/openmed/OpenMed-PII-LiteClinical-Base-66M-v1}
}

Links

• Organization: OpenMed