mmbert32k pii detector merged EU AI Act Compliance Profile

mmBERT-32K PII Detector (Merged)

Full merged model for PII (Personally Identifiable Information) detection, ready for direct inference. Based on mmBERT-32K-YaRN with 32K context length support.

Model Details

| Property | Value | |----------|-------| | Base Model | llm-semantic-router/mmbert-32k-yarn | | Architecture | ModernBERT (Flash Attention 2) | | Parameters | 307M | | Task | Token Classification (NER) | | Max Context | 32,768 tokens | | Entity Types | 17 PII types (35 BIO labels) |

Supported PII Types

• PERSON - Person names (98.7% accuracy)
• EMAIL_ADDRESS - Email addresses (95%+ accuracy)
• PHONE_NUMBER - Phone numbers (99.1% accuracy)
• STREET_ADDRESS - Street addresses (95.9% accuracy)
• CREDIT_CARD - Credit card numbers (84% accuracy)
• 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
• LoRA Rank: 32 (merged into full model)

Usage

from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

model = AutoModelForTokenClassification.from_pretrained(
    "llm-semantic-router/mmbert32k-pii-detector-merged"
)
tokenizer = AutoTokenizer.from_pretrained(
    "llm-semantic-router/mmbert32k-pii-detector-merged"
)

text = "My email is john.smith@example.com and phone is 555-123-4567"
inputs = tokenizer(text, return_tensors="pt", truncation=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch

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Base Model