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OpenMed/
OpenMed-PII-BiomedBERT-Base-110M-v1

Name: OpenMed-PII-BiomedBERT-Base-110M-v1
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OpenMed-PII-BiomedBERT-Base-110M-v1

PII Detection Model | 110M Parameters | Open Source

Model Description

OpenMed-PII-BiomedBERT-Base-110M-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	0.9497
Precision	0.9537
Recall	0.9457
Macro F1	0.9489
Weighted F1	0.9488
Accuracy	0.9932

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
`credit_debit_card`	1.000	1.000	1.000	214
`biometric_identifier`	0.998	0.996	1.000	234
`health_plan_beneficiary_number`	0.998	0.995	1.000	216
`email`	0.995	0.997	0.992	761
`ssn`	0.993	1.000	0.986	141

Challenging Entities

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

Entity	F1	Precision	Recall	Support
`time`	0.868	0.928	0.815	471
`pin`	0.851	0.842	0.860	136
`sexuality`	0.809	0.731	0.905	84
`gender`	0.776	0.712	0.852	189
`occupation`	0.676	0.741	0.622	719

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

python

from transformers import pipeline

# Load the PII detection pipeline
ner = pipeline("ner", model="openmed/OpenMed-PII-BiomedBERT-Base-110M-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

python

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

python

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model_name = "openmed/OpenMed-PII-BiomedBERT-Base-110M-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

bibtex

@misc{openmed-pii-2026,
  title = {OpenMed-PII-BiomedBERT-Base-110M-v1: PII Detection Model},
  author = {OpenMed Science},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/openmed/OpenMed-PII-BiomedBERT-Base-110M-v1}
}

OpenMed/OpenMed-PII-BiomedBERT-Base-110M-v1

OpenMed-PII-BiomedBERT-Base-110M-v1

Model Description

Key Features

Performance

Top 10 PII Models

Best Performing Entities

Challenging Entities

Supported Entity Types

Usage

Quick Start

De-identification Example

Batch Processing

Training Details

Dataset

Training Configuration

Intended Use & Limitations

Intended Use

Limitations

Citation

Links

OpenMed/
OpenMed-PII-BiomedBERT-Base-110M-v1