---
language: en
license: mit
library_name: sklearn
tags:
  - token-classification
  - ner
  - pii-detection
  - sklearn
datasets:
  - zachz/pii-detection-corpus
pipeline_tag: token-classification
---

# PII NER Model

A lightweight sklearn-based Named Entity Recognition model for detecting Personally Identifiable Information in text.

## Model Details

- **Type:** Dict Vectorizer + Logistic Regression pipeline
- **Task:** Token-level NER classification
- **Framework:** scikit-learn
- **Labels:** O, NAME, EMAIL, PHONE, SSN

## Usage

```python
import pickle

with open("model.pkl", "rb") as f:
    model = pickle.load(f)

def extract_features(tokens, idx):
    token = tokens[idx]
    features = {
        'word.lower': token.lower(),
        'word.length': len(token),
        'word.has_at': '@' in token,
        'word.is_digit': token.isdigit(),
    }
    if idx > 0: features['prev.lower'] = tokens[idx-1].lower()
    if idx < len(tokens)-1: features['next.lower'] = tokens[idx+1].lower()
    return features

text = "Contact jane@test.com or call 555-123-4567"
tokens = text.split()
features = [extract_features(tokens, i) for i in range(len(tokens))]
predictions = model.predict(features)
entities = [(t, l) for t, l in zip(tokens, predictions) if l != "O"]
```

## Limitations

- Small training set (12 examples)
- Simple whitespace tokenization
- English only
- Best used as a lightweight first-pass PII detector

## License

MIT