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darwinkernelpanic/moderat

sourceHugging Facemitupdated 8mo agoView on Hugging Face
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moderat - Dual-Mode Content Moderation + PII Filter

A text classification model for content moderation with age-appropriate filtering and PII detection.

Features

  • —Dual-mode filtering: <13 (strict) vs 13+ (laxed)
  • —6 content categories: Safe, Harassment, Swearing (reaction), Swearing (aggressive), Hate Speech, Spam
  • —PII Detection: Emails, phones, addresses, credit cards, SSN
  • —Unicode Deobfuscation: Detects circled letters (ⓕ), double-struck (ℂ), fullwidth, mathematical symbols
  • —Social Media Protection:
  • —<13: Block all social media sharing
  • —13+: Allow, block only if grooming detected
  • —Grooming Detection: Keywords like "dm me", "don't tell parents", "our secret"

Quick Start

python
from pii_extension import CombinedModerationFilter

filter = CombinedModerationFilter("darwinkernelpanic/moderat")

# Content moderation
result = filter.check("damn that's crazy", age=15)
# -> ALLOWED (reaction swearing for 13+)

# PII blocking (all ages)
result = filter.check("My email is test@gmail.com", age=15)
# -> BLOCKED (PII detected)

# Social media (13+ allowed)
result = filter.check("Follow me on instagram @user", age=15)
# -> ALLOWED

# Grooming detection
result = filter.check("DM me privately, don't tell parents", age=14)
# -> BLOCKED (grooming detected)

Unicode Deobfuscation

Automatically detects and normalizes unicode bypass attempts:

TechniqueExampleNormalized
Circled lettersⓕⓤⓒⓚfuck
Double-struckℂℍCH
FullwidthＦF
Mathematical𝐟f

All obfuscated text is normalized before moderation checks.

Social Media Rules

AgePlain ShareWith Grooming Context
<13❌ Blocked❌ Blocked
13+✅ Allowed❌ Blocked

Grooming keywords: "dm me", "don't tell", "secret", "send pics", "meet up", etc.

Content Labels

Text<1313+
"damn that's crazy"❌ Blocked✅ Allowed
"shit that sucks"❌ Blocked✅ Allowed
"you're trash"❌ Blocked❌ Blocked
"kill yourself"❌ Blocked❌ Blocked

Model Details

  • —Algorithm: Multinomial Naive Bayes + TF-IDF + Regex PII
  • —Content accuracy: 77%
  • —PII detection: Regex-based (fast, no ML)
  • —Features: 10,000 max, 1-3 ngrams

Files

  • —moderation_model.pkl - Content moderation model
  • —pii_extension.py - PII + grooming detection
  • —inference.py - Basic inference
  • —moderat_speed_test.ipynb - Colab notebook

Colab

Test it: Open in Colab

Speed

  • —Single inference: ~2-5ms
  • —With PII check: ~3-7ms
  • —Throughput: ~300-500 texts/sec