Fsoft-AIC/pii-phi

Overview

pii-phi is a fine-tuned version of Phi-3.5-mini-instruct designed to extract Personally Identifiable Information (PII) from unstructured text. The model outputs PII entities in a structured JSON format according to strict schema guidelines.

Training Prompt Format

# GUIDELINES
- Extract all instances of the following Personally Identifiable Information (PII) entities from the provided text and return them in JSON format.
- Each item in the JSON list should include an 'entity' key specifying the type of PII and a 'value' key containing the extracted information.
- The supported entities are: PERSON_NAME, BUSINESS_NAME, API_KEY, USERNAME, API_ENDPOINT, WEBSITE_ADDRESS, PHONE_NUMBER, EMAIL_ADDRESS, ID, PASSWORD, ADDRESS.

# EXPECTED OUTPUT
- The json output must be in the format below:
{
    "result": [
        {"entity": "ENTITY_TYPE", "value": "EXTRACTED_VALUE"},
        ...
    ]
}

Supported Entities

• PERSON_NAME
• BUSINESS_NAME
• API_KEY
• USERNAME
• API_ENDPOINT
• WEBSITE_ADDRESS
• PHONE_NUMBER
• EMAIL_ADDRESS
• ID
• PASSWORD
• ADDRESS

Intended Use

The model is intended for PII detection in text documents to support tasks such as data anonymization, compliance, and security auditing.

Limitations

• Not guaranteed to detect all forms of PII in every context.
• May return false positives or omit contextually relevant information.

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Installation

Install the vllm package to run the model efficiently:

pip install vllm

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Example:

from vllm import LLM, SamplingParams

llm = LLM("Fsoft-AIC/pii-phi")

system_prompt = """
# GUIDELINES
- Extract all instances of the following Personally Identifiable Information (PII) entities from the provided text and return them in JSON format.
- Each item in the JSON list should include an 'entity' key specifying the type of PII and a 'value' key containing the extracted information.
- The supported entities are: PERSON_NAME, BUSINESS_NAME, API_KEY, USERNAME, API_ENDPOINT, WEBSITE_ADDRESS, PHONE_NUMBER, EMAIL_ADDRESS, ID, PASSWORD, ADDRESS.

# EXPECTED OUTPUT
- The json output must be in the format below:
{
    "result": [
        {"entity": "ENTITY_TYPE", "value": "EXTRACTED_VALUE"},
        ...
    ]
}
"""
pii_message = "I am James Jake and my employee number is 123123123"

sampling_params = SamplingParams(temperature=0, max_tokens=1000)
outputs = llm.chat(
    [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": pii_message},
    ],
    sampling_params,
)


for output in outputs:
    generated_text = output.outputs[0].text
    print(generated_text)