OpenMed-PII-French-QwenMed-XLarge-600M-v1

What it does

This model performs token classification to identify 54 types of personal identifiers, including names, addresses, and social security numbers, in French clinical and biomedical text. It reports an F1 score of 0.851 on the French subset of the AI4Privacy benchmark, as reported by the author.

Best for

• De-identifying French medical records
• Anonymizing research notes and discharge summaries
• Extracting personal identifiers from French clinical text

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

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-PII-French-QwenMed-XLarge-600M-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-French-QwenMed-XLarge-600M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())