Deberta_finetuned_pii open-source model - Accurately identify and classify personal identity information in unstructured text

Deberta Finetuned Pii

Developed by h2oai

A fine-tuned model specifically optimized for identifying and classifying Personally Identifiable Information (PII) in unstructured text data.

Sequence Labeling

Transformers

EnglishOpen Source License:MIT #PII Precision Identification #Privacy Compliance Detection #Multi-category Sensitive Information

Downloads 7,506

Release Time : 7/2/2024

Model Overview

This model accurately identifies multiple PII categories, including account names, credit card numbers, emails, phone numbers, and addresses, ensuring data privacy and compliance.

Model Features

Multi-category PII Identification

Supports identifying various PII categories such as account information, banking details, personal information, contact details, professional data, financial data, digital identifiers, and online footprints.

High-precision Detection

Efficiently detects and categorizes sensitive information in unstructured text data, ensuring data privacy and compliance.

Broad Applicability

Suitable for PII detection needs in various scenarios, including documents, emails, and user-generated content.

Model Capabilities

Identify Personally Identifiable Information

Classify sensitive data

Detect PII in unstructured text

Use Cases

Data Privacy and Compliance

Document Sensitive Information Detection

Automatically detects Personally Identifiable Information in documents to ensure compliance with data privacy regulations.

Accurately identifies multiple PII categories, reducing the risk of data breaches.

User-generated Content Review

Scans user-submitted content to identify and classify sensitive information.

Improves content review efficiency and protects user privacy.

Financial Security

Financial Transaction Record Analysis

Identifies sensitive data such as account information and credit card numbers in transaction records.

Prevents financial fraud and ensures transaction security.

🚀 PII Identifier Model

A finetuned model for recognizing and classifying Personally Identifiable Information (PII) in unstructured text data.

🚀 Quick Start

A finetuned model designed to recognize and classify Personally Identifiable Information (PII) within unstructured text data. This powerful model accurately identifies a wide range of PII categories, such as account names, credit card numbers, emails, phone numbers, and addresses. The model is specifically trained to detect various PII types, including but not limited to:

Property	Details
Model Type	Token Classification
Training Data	Not specified

Category	Data
Account-related information	Account name, account number, and transaction amounts
Banking details	BIC, IBAN, and Bitcoin or Ethereum addresses
Personal information	Full name, first name, middle name, last name, gender, and date of birth
Contact information	Email, phone number, and street address (including building number, city, county, state, and zip code)
Job-related data	Job title, job area, job descriptor, and job type
Financial data	Credit card number, issuer, CVV, and currency information (code, name, and symbol)
Digital identifiers	IP addresses (IPv4 and IPv6), MAC addresses, and user agents
Online presence	URL, usernames, and passwords
Other sensitive data	SSN, vehicle VIN and VRM, phone IMEI, and nearby GPS coordinates

The PII Identifier Model ensures data privacy and compliance by effectively detecting and categorizing sensitive information within documents, emails, user-generated content, and more. Make your data processing safer and more secure with our state-of-the-art PII detection technology.

💻 Usage Examples

Basic Usage

# How to do Inference
from transformers import pipeline
gen = pipeline("token-classification", "h2oai/deberta_finetuned_pii", device=-1)

text = "My name is John and I live in California."
output = gen(text, aggregation_strategy="first")

📄 License

This project is licensed under the MIT License.

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