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

What it does

This model performs token classification to identify personal identifiers such as names, addresses, and social security numbers in Portuguese text. It reports an F1 score of 0.875 on the AI4Privacy and synthetic Portuguese PII test set, as reported by the author.

Best for

• De-identifying Portuguese medical records
• Removing personal data from clinical notes
• Anonymizing Portuguese research documents

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 Portuguese Data: ~80K high-quality samples generated with locale-specific formatting (CPF format, +55 phones, Brazilian names, R$ currency)

Use it

pip install transformers torch

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