llm-semantic-router/mmbert32k-pii-detector-lora

mmBERT-32K PII Detector LoRA

LoRA adapter for PII (Personally Identifiable Information) detection using mmBERT-32K-YaRN base model with 32K context length.

Model Details

Supported PII Types

• —PERSON - Person names
• —EMAIL_ADDRESS - Email addresses
• —PHONE_NUMBER - Phone numbers
• —STREET_ADDRESS - Street addresses
• —CREDIT_CARD - Credit card numbers
• —US_SSN - US Social Security Numbers
• —US_DRIVER_LICENSE - US Driver License numbers
• —IBAN_CODE - International Bank Account Numbers
• —IP_ADDRESS - IP addresses
• —DATE_TIME - Dates and times
• —AGE - Age information
• —ORGANIZATION - Organization names
• —GPE - Geopolitical entities
• —ZIP_CODE - ZIP/postal codes
• —DOMAIN_NAME - Domain names
• —NRP - Nationalities, religious or political groups
• —TITLE - Titles (Mr., Dr., etc.)

Training

• —Dataset: Microsoft Presidio research dataset
• —Epochs: 5
• —Batch Size: 16
• —Learning Rate: 1e-4
• —Training Samples: ~5000

Usage

from peft import PeftModel
from transformers import AutoModelForTokenClassification, AutoTokenizer

# Load base model and LoRA adapter
base_model = AutoModelForTokenClassification.from_pretrained(
    "llm-semantic-router/mmbert-32k-yarn",
    num_labels=35
)
model = PeftModel.from_pretrained(base_model, "llm-semantic-router/mmbert32k-pii-detector-lora")
tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/mmbert32k-pii-detector-lora")

License

MIT License