ab-ai / pii_model

Personal Identifiable Information (PII Model)

This model is a fine-tuned version of bert-base-cased on the generator dataset. It achieves the following results:

• Training Loss: 0.003900
• Validation Loss: 0.051071
• Precision: 95.53%
• Recall: 96.60%
• F1: 96%
• Accuracy:99.11%

Model description

Meet our digital safeguard, a savvy token classification model with a knack for spotting personally identifiable information (PII) entities. Trained on the illustrious Bert architecture and fine-tuned on a custom dataset, this model is like a superhero for privacy, swiftly detecting names, addresses, dates of birth, and more. With each token it encounters, it acts as a vigilant guardian, ensuring that sensitive information remains shielded from prying eyes, making the digital realm a safer and more secure place to explore.

Model can Detect Following Entity Group

• ACCOUNTNUMBER
• FIRSTNAME
• ACCOUNTNAME
• PHONENUMBER
• CREDITCARDCVV
• CREDITCARDISSUER
• PREFIX
• LASTNAME
• AMOUNT
• DATE
• DOB
• COMPANYNAME
• BUILDINGNUMBER
• STREET
• SECONDARYADDRESS
• STATE
• EMAIL
• CITY
• CREDITCARDNUMBER
• SSN
• URL
• USERNAME
• PASSWORD
• COUNTY
• PIN
• MIDDLENAME
• IBAN
• GENDER
• AGE
• ZIPCODE
• SEX

Training hyperparameters

The following hyperparameters were used during training:

Training results

Author

[email protected]

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2