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

What it does

This model identifies and classifies 54 types of personal identifiers, including names, addresses, and social security numbers, in Chinese clinical and biomedical text. It reports an F1 score of 0.753 on the AI4Privacy and synthetic Chinese PII test set, as stated by the author.

Best for

• De-identifying Chinese medical records
• Anonymizing patient data in research
• Filtering personal data from Chinese clinical notes

What it detects (54 labels)

• Account name
• Age
• Amount
• Bank account
• BIC code
• Bitcoin address
• Building number
• City
• County
• Credit card number
• Credit card issuer
• Currency
• Currency code
• Currency name
• Currency symbol
• Card CVV
• Date
• Date of birth
• Email
• Ethereum address
• Eye color
• First name
• Gender
• GPS coordinates
• Height
• IBAN
• IMEI
• IP address
• Job department
• Job title
• Last name
• Litecoin address
• MAC address
• Masked number
• Middle name
• Occupation
• Ordinal direction
• Organization
• Password
• Phone
• PIN
• Prefix
• Secondary address
• Sex
• Social security number
• State
• Street
• Time
• URL
• User agent
• Username
• Vehicle identification number
• Vehicle registration
• ZIP code

Quality

This model was trained on a combination of: - AI4Privacy PII Masking 200K: Multilingual base dataset (200K records across 8 languages) - NVIDIA Nemotron-PII: Seed dataset for synthetic data generation - Synthetic Chinese Data: ~25K high-quality samples generated with locale-specific formatting (身份证号 format, +86 phones, Chinese names, ¥ currency)

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-PII-Chinese-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-Chinese-ModernMed-Large-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())