from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

# Load model and tokenizer
model_name = "SoelMgd/bert-pii-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Create NER pipeline
ner_pipeline = pipeline(
    "ner", 
    model=model, 
    tokenizer=tokenizer,
    aggregation_strategy="simple"
)

# Example usage
text = "Hi, my name is John Smith and my email is john.smith@company.com"
entities = ner_pipeline(text)
print(entities)

Training Data

Dataset: AI4Privacy PII-200k
Size: ~209k examples
Languages: English, French, German, Italian (this model: English only)
Entity Types: 56 different PII categories

Performance

The model achieves high performance on PII detection tasks with good precision and recall across different entity types.

Intended Use

This model is designed for:

PII detection and masking in text
Privacy compliance applications
Data anonymization pipelines
Content moderation systems

Limitations

Trained primarily on English text
May not generalize to domain-specific jargon
Performance may vary on very short or very long texts
Should be validated on your specific use case