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license: mit
language:
- en
pipeline_tag: text-generation
tags:
- LLM
- token classification
- nlp
- safetensor
- PyTorch
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: transformers
widget:
- text: My name is Sylvain and I live in Paris
example_title: Parisian
- text: My name is Sarah and I live in London
example_title: Londoner
---
# PII Detection Model - Phi3 Mini Fine-Tuned
This repository contains a fine-tuned version of the [Phi3 Mini](https://huggingface.co/ab-ai/PII-Model-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](https://huggingface.co/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
```bash
### 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:
```bash
pip install transformers
```
### Run Inference
```bash
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']}
``` |