Token Classification
GLiNER
PyTorch
English
Vietnamese
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@@ -8,6 +8,96 @@ pipeline_tag: token-classification
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  datasets:
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  - urchade/synthetic-pii-ner-mistral-v1
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Model Card for GLiNER PII
@@ -16,8 +106,6 @@ GLiNER is a Named Entity Recognition (NER) model capable of identifying any enti
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  This model has been trained by fine-tuning `urchade/gliner_multi-v2.1` on the `urchade/synthetic-pii-ner-mistral-v1` dataset.
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- 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`.
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-
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  ## Links
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  * Paper: https://arxiv.org/abs/2311.08526
 
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  datasets:
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  - urchade/synthetic-pii-ner-mistral-v1
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  ---
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+ # Entity Types Classification
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+
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+ ## Personal Information
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+ - Date of birth
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+ - Age
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+ - Gender
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+ - Last name
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+ - Occupation
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+ - Education level
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+ - Phone number
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+ - Email
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+ - Street address
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+ - City
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+ - Country
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+ - Postcode
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+ - User name
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+ - Password
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+ - Tax ID
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+ - License plate
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+ - CVV
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+ - Bank routing number
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+ - Account number
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+ - SWIFT BIC
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+ - Biometric identifier
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+ - Device identifier
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+ - Location
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+
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+ ## Financial Information
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+ - Account number
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+ - Bank routing number
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+ - SWIFT BIC
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+ - CVV
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+ - Tax ID
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+ - API key
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+
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+ ## Health and Medical Information
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+ - Blood type
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+ - Biometric identifier
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+ - Organ
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+ - Diseases symptom
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+ - Diagnostics
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+ - Preventive medicine
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+ - Treatment
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+ - Surgery
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+ - Drug chemical
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+ - Medical device technique
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+ - Personal care
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+
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+ ## Online and Web-related Information
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+ - URL
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+ - IP address
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+ - Email
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+ - User name
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+ - API key
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+
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+ ## Professional Information
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+ - Occupation
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+ - Skill
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+ - Organization
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+ - Company name
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+
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+ ## Location Information
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+ - City
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+ - Country
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+ - Postcode
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+ - Street address
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+ - Location
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+
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+ ## Time-Related Information
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+ - Date
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+ - Date time
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+
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+ ## Miscellaneous
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+ - Event
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+ - Miscellaneous
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+
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+ ## Product and Goods Information
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+ - Product
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+ - Quantity
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+ - Food drink
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+ - Transportation
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+
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+ ## Identifiers
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+ - Device identifier
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+ - Biometric identifier
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+ - User name
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+ - Email
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+ - Phone number
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+ - URL
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+ - License plate
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  # Model Card for GLiNER PII
 
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  This model has been trained by fine-tuning `urchade/gliner_multi-v2.1` on the `urchade/synthetic-pii-ner-mistral-v1` dataset.
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  ## Links
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  * Paper: https://arxiv.org/abs/2311.08526