ab-ai/PII-Model-Phi3-Mini

PII Detection Model - Phi3 Mini Fine-Tuned

This repository contains a fine-tuned version of the Phi3 Mini model for detecting personally identifiable information (PII). The model has been specifically trained to recognize various PII entities in text, making it a powerful tool for tasks such as data redaction, privacy protection, and compliance with data protection regulations.

Model Overview

Model Architecture

• Base Model: Phi3 Mini
• Fine-Tuned For: PII detection
• Framework: Hugging Face Transformers

Detected PII Entities

The model is capable of detecting the following PII entities:

• Personal Information:

  • firstname
  • middlename
  • lastname
  • sex
  • dob (Date of Birth)
  • age
  • gender
  • height
  • eyecolor

• Contact Information:

  • email
  • phonenumber
  • url
  • username
  • useragent

• Address Information:

  • street
  • city
  • state
  • county
  • zipcode
  • country
  • secondaryaddress
  • buildingnumber
  • ordinaldirection

• Geographical Information:

  • nearbygpscoordinate

• Organizational Information:

  • companyname
  • jobtitle
  • jobarea
  • jobtype

• Financial Information:

  • accountname
  • accountnumber
  • creditcardnumber
  • creditcardcvv
  • creditcardissuer
  • iban
  • bic
  • currency
  • currencyname
  • currencysymbol
  • currencycode
  • amount

• Unique Identifiers:

  • pin
  • ssn
  • imei (Phone IMEI)
  • mac (MAC Address)
  • vehiclevin (Vehicle VIN)
  • vehiclevrm (Vehicle VRM)

• Cryptocurrency Information:

  • bitcoinaddress
  • litecoinaddress
  • ethereumaddress

• Other Information:

  • ip (IP Address)
  • ipv4
  • ipv6
  • maskednumber
  • password
  • time
  • ordinaldirection
  • prefix

Prompt Format

### Instruction:
  Identify and extract the following PII entities from the text, if present: companyname, pin, currencyname, email, phoneimei, litecoinaddress, currency, eyecolor, street, mac, state, time, vehiclevin, jobarea, date, bic, currencysymbol, currencycode, age, nearbygpscoordinate, amount, ssn, ethereumaddress, zipcode, buildingnumber, dob, firstname, middlename, ordinaldirection, jobtitle, bitcoinaddress, jobtype, phonenumber, height, password, ip, useragent, accountname, city, gender, secondaryaddress, iban, sex, prefix, ipv4, maskednumber, url, username, lastname, creditcardcvv, county, vehiclevrm, ipv6, creditcardissuer, accountnumber, creditcardnumber. Return the output in JSON format.

### Input:
  Greetings, Mason! Let's celebrate another year of wellness on 14/01/1977. Don't miss the event at 176,Apt. 388.

### Output:

Usage

Installation

To use this model, you'll need to have the transformers library installed:

pip install transformers

Run Inference

from transformers import AutoTokenizer, AutoModelForTokenClassification

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("ab-ai/PII-Model-Phi3-Mini")
model = AutoModelForTokenClassification.from_pretrained("ab-ai/PII-Model-Phi3-Mini")


input_text = "Hi Abner, just a reminder that your next primary care appointment is on 23/03/1926. Please confirm by replying to this email Nathen15@hotmail.com."

model_prompt = f"""### Instruction:
    Identify and extract the following PII entities from the text, if present: companyname, pin, currencyname, email, phoneimei, litecoinaddress, currency, eyecolor, street, mac, state, time, vehiclevin, jobarea, date, bic, currencysymbol, currencycode, age, nearbygpscoordinate, amount, ssn, ethereumaddress, zipcode, buildingnumber, dob, firstname, middlename, ordinaldirection, jobtitle, bitcoinaddress, jobtype, phonenumber, height, password, ip, useragent, accountname, city, gender, secondaryaddress, iban, sex, prefix, ipv4, maskednumber, url, username, lastname, creditcardcvv, county, vehiclevrm, ipv6, creditcardissuer, accountnumber, creditcardnumber. Return the output in JSON format.

    ### Input:
    {input_text}

    ### Output: """


inputs = tokenizer(model_prompt, return_tensors="pt").to(device)
# adjust max_new_tokens according to your need
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=120)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response) #{'middlename': ['Abner'], 'dob': ['23/03/1926'], 'email': ['Nathen15@hotmail.com']}