Pii Entity Extractor

🚀 Model Card for PII Detection with DeBERTa

This model is a fine - tuned version of DeBERTa for Named Entity Recognition (NER), specifically designed to detect Personally Identifiable Information (PII) in text.

🚀 Quick Start

This model is a fine - tuned version of microsoft/deberta for Named Entity Recognition (NER). It's specifically crafted to detect Personally Identifiable Information (PII) entities such as names, SSNs, phone numbers, credit card numbers, addresses, and more.

✨ Features

• Based on the powerful DeBERTa architecture.
• Specialized for PII detection through fine - tuning on a custom dataset.
• Performs sequence labeling using token - level classification.

📦 Installation

To use this model, you need to install the transformers library. You can install it using pip:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

model_name = "AI-Enthusiast11/pii-entity-extractor"  
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Post processing logic to combine the subword tokens
def merge_tokens(ner_results):
    entities = {}
    for entity in ner_results:
        entity_type = entity["entity_group"]
        entity_value = entity["word"].replace("##", "")  # Remove subword prefixes

        # Handle token merging
        if entity_type not in entities:
            entities[entity_type] = []
        if entities[entity_type] and not entity_value.startswith(" "):
            # If the previous token exists and this one isn't a new word, merge it
            entities[entity_type][-1] += entity_value
        else:
            entities[entity_type].append(entity_value)

    return entities

def redact_text_with_labels(text):
    ner_results = nlp(text)

    # Merge tokens for multi-token entities (if any)
    cleaned_entities = merge_tokens(ner_results)

    redacted_text = text
    for entity_type, values in cleaned_entities.items():
        for value in values:
            # Replace each identified entity with the label
            redacted_text = redacted_text.replace(value, f"[{entity_type}]")

    return redacted_text


#Loading the pipeline
nlp = pipeline("ner", model=model_name, tokenizer=tokenizer, aggregation_strategy="simple")

# Example input (choose one from your examples)
example = "Hi, I’m Mia Thompson. I recently noticed that my electricity bill hasn’t been updated despite making the payment last week. I used account number 4893172051 linked with routing number 192847561. My service was nearly suspended, and I’d appreciate it if you could verify the payment. You can reach me at 727-814-3902 if more information is needed."

# Run pipeline and process result
ner_results = nlp(example)
cleaned_entities = merge_tokens(ner_results)

# Print the NER results
print("\n==NER Results:==\n")
for entity_type, values in cleaned_entities.items():
    print(f"  {entity_type}: {', '.join(values)}")

# Redact the single example with labels
redacted_example = redact_text_with_labels(example)

# Print the redacted result
print(f"\n==Redacted Example:==\n{redacted_example}")

📚 Documentation

Model Details

Model Description

This transformer - based model is fine - tuned on a custom dataset to detect sensitive information, commonly categorized as PII. The model performs sequence labeling to identify entities using token - level classification.

Training Details

Training Data

The model was fine - tuned on a custom dataset containing labeled examples of the following PII entity types:

• NAME
• SSN
• PHONE - NO
• CREDIT - CARD - NO
• BANK - ACCOUNT - NO
• BANK - ROUTING - NO
• ADDRESS

Epoch Logs

SeqEval Classification Report

Summary

• Micro avg: 0.95
• Macro avg: 0.95
• Weighted avg: 0.95

Evaluation

Testing Data

Evaluation was done on a held - out portion of the same labeled dataset.

Metrics

• Precision
• Recall
• F1 (via seqeval)
• Entity - wise breakdown
• Token - level accuracy

Results

• F1 - score consistently above 0.95 for most labels, showing robustness in PII detection.

Recommendations

⚠️ Important Note

• Use human review in high - risk environments.
• Evaluate on your own domain - specific data before deployment.