OpenMed-PII-ModernMed-Large-395M-v1

What it does

This model identifies and classifies 54 types of personal identifiers, including names, addresses, SSNs, and medical record numbers, in English clinical and biomedical text. It is a 395M parameter ModernBERT model that reports an F1 score of 0.961 on the NVIDIA Nemotron-PII test set.

Best for

• De-identifying clinical notes
• Redacting personal data from research records
• Anonymizing biomedical literature

What it detects (54 labels)

• Account number
• Age
• API key
• Bank routing number
• Biometric identifier
• Blood type
• Certificate license number
• City
• Company name
• Coordinate
• Country
• County
• Credit debit card
• Customer ID
• Card CVV
• Date
• Date of birth
• Date time
• Device identifier
• Education level
• Email
• Employee ID
• Employment status
• Fax number
• First name
• Gender
• Health plan beneficiary number
• HTTP cookie
• IPv4 address
• IPv6 address
• Language
• Last name
• License plate
• MAC address
• Medical record number
• Occupation
• Password
• Phone number
• PIN
• Political view
• Postcode
• Race ethnicity
• Religious belief
• Sexuality
• Social security number
• State
• Street address
• SWIFT/BIC code
• Tax ID
• Time
• Unique ID
• URL
• Username
• Vehicle identifier

Quality

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-PII-ModernMed-Large-395M-v1", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))

pip install --upgrade "openmed[onnx-runtime]"

from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-PII-ModernMed-Large-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())