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

What it does

This token classification model identifies personal identifiers such as names, addresses, and social security numbers in Hindi clinical and biomedical text. It reports an F1 score of 0.777 on the Hindi subset of the AI4Privacy dataset, with 596M parameters and a 512-token sequence length.

Best for

• De-identifying Hindi medical records
• Redacting personal data from clinical notes
• Anonymizing Hindi biomedical 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

Use it

pip install transformers torch

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