Tegmen

A high-performance on-premise PII detection and masking solution

Overview

Tegmen is a production-ready token classification system designed for identifying and masking personally identifiable information (PII) in text data. Built for high-throughput data sanitization workflows, it offers on-premise deployment capabilities with enterprise-grade performance.

Key Features

• On-Premise Deployment: Run entirely within your infrastructure
• Lightweight Architecture: Optimized for edge deployment
• Fine-Tunable: Easily adapt to your specific data distributions
• Long Context Support: Process documents up to 128,000 tokens
• Configurable Detection: Tune precision/recall tradeoffs

Supported PII Categories

The model detects 8 categories of sensitive information:

Category	Description
account_number	Financial account identifiers
private_address	Physical and mailing addresses
private_email	Email addresses
private_person	Personal names
private_phone	Phone numbers
private_url	URLs and web addresses
private_date	Birth dates and personal dates
secret	API keys, passwords, credentials

Installation

pip install transformers torch

Quick Start

Using the Pipeline API

from transformers import pipeline

detector = pipeline("token-classification", model="comethrusws/tegmen", aggregation_strategy="simple")

text = "Contact John Smith at john.smith@email.com"
results = detector(text)

for item in results:
    print(f"Found: {item['word']} ({item['entity_group']})")

Using the Model Directly

import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("comethrusws/tegmen")
model = AutoModelForTokenClassification.from_pretrained("comethrusws/tegmen")

text = "My name is Alice and my email is alice@example.com"
inputs = tokenizer(text, return_tensors="pt")

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

predictions = outputs.logits.argmax(dim=-1)
labels = [model.config.id2label[p.item()] for p in predictions[0]]
print(labels)

Performance Specifications

• Architecture: Transformer encoder
• Parameters: 1.5B total / 50M active
• Context Window: 128,000 tokens
• Output Format: BIOES span tagging

License

Apache License 2.0

Support

For enterprise support, contact SAGEA.