OpenMed-PII-Italian-BiomedBERTFull-Base-110M-v1

What it does

This token classification model detects personal identifiers such as names, dates, and phone numbers in Italian clinical and biomedical text. It is built on Microsoft's BiomedBERT base model and reports an F1 score of 0.925 on the Italian subset of the AI4Privacy benchmark.

Best for

• De-identifying Italian clinical notes
• Redacting personal data from research records
• Anonymizing Italian biomedical 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-Italian-BiomedBERTFull-Base-110M-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-Italian-BiomedBERTFull-Base-110M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())

pip install "openmed[mlx]"

from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text("Patient started imatinib in March.", model_name="OpenMed/OpenMed-PII-Italian-BiomedBERTFull-Base-110M-v1", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)