license: apache-2.0
language:
- en
- fr
- de
- es
- pt
- it
library_name: gliner
pipeline_tag: token-classification
datasets:
- urchade/synthetic-pii-ner-mistral-v1
Model Card for GLiNER PII
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
This model has been trained by fine-tuning urchade/gliner_multi-v2.1
on the urchade/synthetic-pii-ner-mistral-v1
dataset.
This model is capable of recognizing various types of personally identifiable information (PII), including but not limited to these entity types: person
, organization
, phone number
, address
, passport number
, email
, credit card number
, social security number
, health insurance id number
, date of birth
, mobile phone number
, bank account number
, medication
, cpf
, driver's license number
, tax identification number
, medical condition
, identity card number
, national id number
, ip address
, email address
, iban
, credit card expiration date
, username
, health insurance number
, registration number
, student id number
, insurance number
, flight number
, landline phone number
, blood type
, cvv
, reservation number
, digital signature
, social media handle
, license plate number
, cnpj
, postal code
, passport_number
, serial number
, vehicle registration number
, credit card brand
, fax number
, visa number
, insurance company
, identity document number
, transaction number
, national health insurance number
, cvc
, birth certificate number
, train ticket number
, passport expiration date
, and social_security_number
.
Links
- Paper: https://arxiv.org/abs/2311.08526
- Repository: https://github.com/urchade/GLiNER
from gliner import GLiNER
model = GLiNER.from_pretrained("urchade/gliner_multi_pii-v1")
text = """
Harilala Rasoanaivo, un homme d'affaires local d'Antananarivo, a enregistré une nouvelle société nommée "Rasoanaivo Enterprises" au Lot II M 92 Antohomadinika. Son numéro est le +261 32 22 345 67, et son adresse électronique est [email protected]. Il a fourni son numéro de sécu 501-02-1234 pour l'enregistrement.
"""
labels = ["work", "booking number", "personally identifiable information", "driver licence", "person", "book", "full address", "company", "actor", "character", "email", "passport number", "Social Security Number", "phone number"]
entities = model.predict_entities(text, labels)
for entity in entities:
print(entity["text"], "=>", entity["label"])
Harilala Rasoanaivo => person
Rasoanaivo Enterprises => company
Lot II M 92 Antohomadinika => full address
+261 32 22 345 67 => phone number
[email protected] => email
501-02-1234 => Social Security Number