pii-ner-nemotron

Model summary

PII NER model trained on nemotron dataset for multilingual PII entity extraction.

• Base model: xlm-roberta-large
• Repository: scanpatch/pii-ner-nemotron
• Training run name: pii-ner-nemotron
• Export timestamp (UTC): 2025-12-29T12:06:13.731145+00:00

Labels

Entity types

• address
• address_apartment
• address_building
• address_city
• address_country
• address_district
• address_geolocation
• address_house
• address_postal_code
• address_region
• address_street
• date
• document_number
• email
• first_name
• ip
• last_name
• middle_name
• military_individual_number
• mobile_phone
• name
• name_initials
• nickname
• organization
• snils
• tin
• vehicle_number

Evaluation

Metric	Value
test_f1	0.9768405285513023
test_precision	0.9734942064790006
test_recall	0.9802099354987895
test_accuracy	0.9977181928808507
train_runtime	1693.5057
train_samples_per_second	238.116

How to use

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="scanpatch/pii-ner-nemotron",
    aggregation_strategy="simple",
)

text = "Contact me at test@example.com and my phone is +380 67 123 45 67."
print(ner(text))