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Cbezz/
modernbert-ausmed-pii

Name: modernbert-ausmed-pii
Availability: InStock

safetensors

modernbert

token-classification

named-entity-recognition

pii

answerdotai/ModernBERT-base

finetune

apache-2.0

model-index

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modernbert-ausmed-pii

Token-classification (NER) model for PII detection in Australian clinical notes, trained on synthetic data from the pii_datagen pipeline. Labels use the BIO scheme over these entity types:

ABN
ADDRESS
ADVANCE_CARE_DIRECTIVE
AGE
AMBULANCE_MEMBERSHIP_NUMBER
BANKING_FINANCE_DETAILS
CENTRELINK_NUMBER
COUNTRY
DATE
DRIVER_LICENCE_NUMBER
DVA_FILE_NUMBER
EMAIL_ADDRESS
FAX_NUMBER
IHI
LGA
MEDICAL_ENTITY
MEDICAL_PROFESSIONAL_ID
MEDICARE_NUMBER
MRN
NAME
NDIS_NUMBER
OCCUPATION
ORGANISATION_NAME
PASSPORT_NUMBER
PATHOLOGY_ACCESSION_NUMBER
PHONE
POLICE_CORRECTIONS_ID
PRIVATE_HEALTH_INSURANCE_NUMBER
PROVIDER_NUMBER
RADIOLOGY_ACCESSION_NUMBER
SEX_GENDER
TAC_CLAIM_NUMBER
TFN
TIMESTAMP
WORKCOVER_CLAIM

MEDICAL_ENTITY marks eponymous medical terms (Parkinson's disease, Foley catheter); it is a hard-negative class and must never be redacted.

Metrics (seqeval, entity-level)

split	precision	recall	f1
test (model selection)	0.8252	0.9506	0.8835
holdout	0.8239	0.9468	0.8811

Training

data: 17817 synthetic notes from the pii_datagen pipeline (audited)
split: 13351 train / 3541 test (per-epoch model selection) / 925 untouched holdout — deterministic per-doc_id hash, seed 42
selection: best epoch by test-split F1 (epoch 4 of 7 run), early stopping (patience 3), max 15 epochs
context length: 8192 tokens

Usage

python

from transformers import pipeline

ner = pipeline("token-classification", model="Cbezz/modernbert-ausmed-pii", aggregation_strategy="simple")
ner("Patient John Smith, DOB 12/03/1980, MRN 4456219.")