Pii_model Open-source Model - Accurately Identify Personally Identifiable Information (PII) Entities

Pii Model

Developed by ab-ai

A token classification model fine-tuned based on bert-base-cased, excelling in identifying Personally Identifiable Information (PII) entities.

Sequence Labeling

Transformers

EnglishOpen Source License:Apache-2.0 #High-precision PII Recognition #Dedicated to Privacy Protection #Multi-category Entity Detection

Downloads 1,204

Release Time : 3/5/2024

Model Overview

This model acts like a privacy-protecting superhero, swiftly detecting sensitive information such as names, addresses, birthdates, etc., ensuring safety and reliability in the digital world.

Model Features

High-precision Recognition

Achieves a precision of 95.53% and recall of 96.60%, accurately identifying various PII entities.

Broad Coverage

Supports recognition of 30+ PII entity types, including names, addresses, financial information, etc.

Privacy Protection

Designed specifically for sensitive information detection, helping organizations and individuals protect private data.

Model Capabilities

Identify Personally Identifiable Information

Token Classification

Sensitive Data Detection

Use Cases

Data Privacy Protection

Document Sensitive Information Scanning

Automatically scans documents for PII to facilitate anonymization

Reduces manual review time and improves compliance

Log Monitoring

Real-time monitoring of sensitive information leaks in system logs

Timely detection and prevention of data breach risks

Compliance Checks

GDPR Compliance Check

Identifies PII in stored data to meet GDPR requirements

Simplifies compliance audit processes

🚀 Personal Identifiable Information (PII Model)

This model is a fine - tuned version of [bert - base - cased](https://huggingface.co/bert - base - cased) on the generator dataset. It's designed to detect personally identifiable information (PII) entities, enhancing privacy and security in the digital world.

🚀 Quick Start

This model is a fine - tuned version of [bert - base - cased](https://huggingface.co/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%

✨ Features

PII Detection: A token classification model that can accurately detect a wide range of personally identifiable information entities.
Fine - Tuned on Bert: Built on the powerful Bert architecture and fine - tuned on a custom dataset for better performance.

📚 Documentation

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:

Property	Details
Learning Rate	5e - 5
Train Batch Size	16
Eval Batch Size	16
Number of Training Epochs	7
Weight Decay	0.01
Save Strategy	Epoch
Load Best Model at End	True
Metric for Best Model	F1
Push to Hub	True
Evaluation Strategy	Epoch
Early Stopping Patience	3

Training results

Epoch	Training Loss	Validation Loss	Precision (%)	Recall (%)	F1 Score (%)	Accuracy (%)
1	0.0443	0.038108	91.88	95.17	93.50	98.80
2	0.0318	0.035728	94.13	96.15	95.13	98.90
3	0.0209	0.032016	94.81	96.42	95.61	99.01
4	0.0154	0.040221	93.87	95.80	94.82	98.88
5	0.0084	0.048183	94.21	96.06	95.13	98.93
6	0.0037	0.052281	94.49	96.60	95.53	99.07

Author

abhijeet__@outlook.com

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

📄 License

This model is licensed under the Apache 2.0 license.

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