Gliner Multi Pii V1

🚀 GLiNER PII Model Card

GLiNER is a Named Entity Recognition (NER) model. It uses a bidirectional transformer encoder (similar to BERT) to identify any entity type. It offers a practical alternative to traditional NER models with predefined entities and Large Language Models (LLMs). LLMs are flexible but costly and large for resource - constrained scenarios.

This model was trained by fine - tuning urchade/gliner_multi - v2.1 on the urchade/synthetic - pii - ner - mistral - v1 dataset. It can recognize various types of personally identifiable information (PII), including many entity types like person, organization, phone number, etc.

🚀 Quick Start

Installation

Since the installation steps are not provided in the original document, this section is skipped.

Usage

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 harilala.rasoanaivo@telma.mg. 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"])

The output of the above code is:

Harilala Rasoanaivo => person
Rasoanaivo Enterprises => company
Lot II M 92 Antohomadinika => full address
+261 32 22 345 67 => phone number
harilala.rasoanaivo@telma.mg => email
501-02-1234 => Social Security Number

✨ Features

• Flexible Entity Recognition: Capable of identifying any entity type, not limited to predefined entities like traditional NER models.
• Cost - effective: A practical alternative to Large Language Models (LLMs) in resource - constrained scenarios.
• PII Recognition: Can recognize a wide range of personally identifiable information (PII) types.

📚 Documentation

Recognizable PII Entities

This model can recognize various types of personally identifiable information (PII), including but not limited to: 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

📄 License

This project is licensed under the Apache - 2.0 license.