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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"name": "moderat-speed-test.ipynb"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🛡️ moderat - Speed Test & Benchmark\n",
"\n",
"Test inference speeds for the dual-mode content moderation model with PII detection.\n",
"\n",
"**Model:** [darwinkernelpanic/moderat](https://huggingface.co/darwinkernelpanic/moderat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 1. Install dependencies\n",
"!pip install -q scikit-learn huggingface-hub"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 2. Download model and files from Hugging Face\n",
"from huggingface_hub import hf_hub_download\n",
"import pickle\n",
"\n",
"MODEL_REPO = \"darwinkernelpanic/moderat\"\n",
"\n",
"# Download model\n",
"model_path = hf_hub_download(\n",
" repo_id=MODEL_REPO,\n",
" filename=\"moderation_model.pkl\"\n",
")\n",
"\n",
"# Download PII extension\n",
"pii_path = hf_hub_download(\n",
" repo_id=MODEL_REPO,\n",
" filename=\"pii_extension.py\"\n",
")\n",
"\n",
"print(f\"✅ Model and PII extension downloaded from {MODEL_REPO}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 3. Import and setup\n",
"import sys\n",
"sys.path.insert(0, pii_path.replace('/pii_extension.py', ''))\n",
"\n",
"from enum import Enum\n",
"import time\n",
"import re\n",
"\n",
"# Load model\n",
"with open(model_path, 'rb') as f:\n",
" pipeline = pickle.load(f)\n",
"\n",
"# Define enums\n",
"class ContentLabel(Enum):\n",
" SAFE = 0\n",
" HARASSMENT = 1\n",
" SWEARING_REACTION = 2\n",
" SWEARING_AGGRESSIVE = 3\n",
" HATE_SPEECH = 4\n",
" SPAM = 5\n",
"\n",
"print(\"✅ Setup complete\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 4. Unicode Deobfuscator\n", "class UnicodeDeobfuscator:\n", " CIRCLED_MAP = {\n", " 'ⓐ': 'a', 'ⓑ': 'b', 'ⓒ': 'c', 'ⓓ': 'd', 'ⓔ': 'e',\n", " 'ⓕ': 'f', 'ⓖ': 'g', 'ⓗ': 'h', 'ⓘ': 'i', 'ⓙ': 'j',\n", " 'ⓚ': 'k', 'ⓛ': 'l', 'ⓜ': 'm', 'ⓝ': 'n', 'ⓞ': 'o',\n", " 'ⓟ': 'p', 'ⓠ': 'q', 'ⓡ': 'r', 'ⓢ': 's', 'ⓣ': 't',\n", " 'ⓤ': 'u', 'ⓥ': 'v', 'ⓦ': 'w', 'ⓧ': 'x', 'ⓨ': 'y', 'ⓩ': 'z',\n", " 'Ⓐ': 'A', 'Ⓑ': 'B', 'Ⓒ': 'C', 'Ⓓ': 'D', 'Ⓔ': 'E',\n", " 'Ⓕ': 'F', 'Ⓖ': 'G', 'Ⓗ': 'H', 'Ⓘ': 'I', 'Ⓙ': 'J',\n", " 'Ⓚ': 'K', 'Ⓛ': 'L', 'Ⓜ': 'M', 'Ⓝ': 'N', 'Ⓞ': 'O',\n", " 'Ⓟ': 'P', 'Ⓠ': 'Q', 'Ⓡ': 'R', 'Ⓢ': 'S', 'Ⓣ': 'T',\n", " 'Ⓤ': 'U', 'Ⓥ': 'V', 'Ⓦ': 'W', 'Ⓧ': 'X', 'Ⓨ': 'Y', 'Ⓩ': 'Z',\n", " }\n", " \n", " @classmethod\n", " def detect(cls, text):\n", " suspicious = []\n", " normalized = []\n", " for char in text:\n", " if char in cls.CIRCLED_MAP:\n", " suspicious.append((char, 'circled'))\n", " normalized.append(cls.CIRCLED_MAP[char])\n", " else:\n", " normalized.append(char)\n", " return len(suspicious) > 0, suspicious, ''.join(normalized)\n", "\n", "# @title 5. PII Detector Class (FIXED)\n",
"class PIIDetector:\n",
" \"\"\"Detect PII with proper age-based social media rules\"\"\"\n",
" \n",
" def __init__(self):\n",
" self.email_pattern = re.compile(r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b')\n",
" self.phone_patterns = [\n",
" re.compile(r'\\b\\d{3}[-.]?\\d{3}[-.]?\\d{4}\\b'),\n",
" re.compile(r'\\b\\(\\d{3}\\)\\s?\\d{3}[-.]?\\d{4}\\b'),\n",
" re.compile(r'\\b\\d{4}\\s?\\d{3}\\s?\\d{3}\\b'),\n",
" re.compile(r'\\b\\d{3}[-.]?\\d{4}\\b'),\n",
" ]\n",
" self.address_pattern = re.compile(r'\\b\\d+\\s+[A-Za-z]+\\s+(?:Street|St|Avenue|Ave|Road|Rd|Lane|Ln|Drive|Dr)\\b', re.IGNORECASE)\n",
" self.cc_pattern = re.compile(r'\\b(?:\\d{4}[-\\s]?){3}\\d{4}\\b|\\b\\d{16}\\b')\n",
" self.social_media_domains = ['instagram.com', 'instagr.am', 'twitter.com', 'x.com', 'tiktok.com', 'snapchat.com', 'discord.com', 'discord.gg']\n",
" self.grooming_keywords = ['dm me', 'private chat', 'dont tell your parents', 'secret', 'send me pics', 'our little secret', 'meet up']\n",
" \n",
" def scan(self, text, age):\n",
" pii_types = []\n",
" text_lower = text.lower()\n",
" \n",
" # Check email\n",
" if self.email_pattern.search(text):\n",
" pii_types.append('email')\n",
" \n",
" # Check phone\n",
" for pattern in self.phone_patterns:\n",
" if pattern.search(text):\n",
" pii_types.append('phone')\n",
" break\n",
" \n",
" # Check address\n",
" if self.address_pattern.search(text):\n",
" pii_types.append('address')\n",
" \n",
" # Check credit card\n",
" if self.cc_pattern.search(text):\n",
" pii_types.append('credit_card')\n",
" \n",
" # Check grooming\n",
" grooming_risk = sum(1 for kw in self.grooming_keywords if kw in text_lower)\n",
" \n",
" # Priority: Critical PII first (blocked for all ages)\n",
" if any(pii in ['email', 'phone', 'address', 'credit_card'] for pii in pii_types):\n",
" return {'blocked': True, 'reason': f'PII detected: {pii_types}', 'pii': pii_types}\n",
" \n",
" # Social media check\n",
" has_social = any(domain in text_lower for domain in self.social_media_domains)\n",
" has_social = has_social or any(x in text_lower for x in ['instagram', 'snapchat', 'discord', 'tiktok'])\n",
" \n",
" if has_social:\n",
" pii_types.append('social_media')\n",
" if age < 13:\n",
" return {'blocked': True, 'reason': 'Social media not allowed under 13', 'pii': pii_types}\n",
" elif grooming_risk > 0:\n",
" return {'blocked': True, 'reason': f'Potential grooming (risk: {grooming_risk})', 'pii': pii_types}\n",
" else:\n",
" return {'blocked': False, 'reason': 'Social media OK for 13+', 'pii': pii_types}\n",
" \n",
" return {'blocked': False, 'reason': 'No PII', 'pii': []}\n",
"\n",
"pii_detector = PIIDetector()\n",
"print(\"✅ PII detector ready (FIXED)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 5. Combined Filter Function\n",
"def check_content(text, age):\n",
" \"\"\"Combined content moderation + PII check\"\"\"\n",
" \n",
" # Step 1: PII Check\n",
" pii_result = pii_detector.scan(text, age)\n",
" if pii_result['blocked']:\n",
" return {\n",
" 'allowed': False,\n",
" 'reason': pii_result['reason'],\n",
" 'violation': 'PII',\n",
" 'pii': pii_result['pii']\n",
" }\n",
" \n",
" # Step 2: Content Moderation\n",
" prediction = pipeline.predict([text])[0]\n",
" probs = pipeline.predict_proba([text])[0]\n",
" confidence = max(probs)\n",
" label = ContentLabel(prediction)\n",
" \n",
" # Age-based rules\n",
" under_13_blocked = [1, 2, 3, 4, 5] # All except SAFE\n",
" teen_plus_blocked = [1, 3, 4, 5] # Allow SWEARING_REACTION\n",
" \n",
" if age >= 13:\n",
" allowed = label.value not in teen_plus_blocked\n",
" else:\n",
" allowed = label.value not in under_13_blocked\n",
" \n",
" # Allow reaction swearing for 13+\n",
" if not allowed and label == ContentLabel.SWEARING_REACTION and age >= 13:\n",
" allowed = True\n",
" reason = 'Swearing permitted as reaction (13+)'\n",
" elif not allowed:\n",
" reason = f'{label.name} detected'\n",
" else:\n",
" reason = 'Safe'\n",
" \n",
" return {\n",
" 'allowed': allowed,\n",
" 'reason': reason,\n",
" 'violation': 'CONTENT' if not allowed else None,\n",
" 'label': label.name,\n",
" 'confidence': confidence,\n",
" 'pii': pii_result.get('pii', [])\n",
" }\n",
"\n",
"print(\"✅ Combined filter ready\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 6. Unicode Deobfuscation Tests\n", "print(\"🔤 Unicode Deobfuscation Tests\\n\")\n", "\n", "unicode_tests = [\n", " (\"have you tried like ⓕ/ ℂk\", 15),\n", " (\"you're a ⓟⓘⓔⓒⓔ of ⓢⓗⓘⓣ\", 15),\n", " (\"ⓚⓘⓛⓛ yourself\", 15),\n", "]\n", "\n", "for text, age in unicode_tests:\n", " is_obf, chars, norm = UnicodeDeobfuscator.detect(text)\n", " result = check_content(text, age)\n", " status = \"✅\" if result['allowed'] else \"❌\"\n", " print(f\"{status} Original: {text}\")\n", " print(f\" Normalized: {norm}\")\n", " print(f\" → {result['reason']}\")\n", " print()\n", "\n", "# @title 7. PII Detection Tests (FIXED)\n",
"print(\"🔒 PII Detection Results (Fixed)\\n\")\n",
"print(\"Expected: Address and Credit Card now detected correctly\")\n",
"print(\"Expected: Social media ALLOWED for 13+ (unless grooming)\\n\")\n",
"print(\"=\"*70)\n",
"\n",
"pii_tests = [\n",
" (\"Contact me at john@example.com\", 15, \"Email - should block\"),\n",
" (\"Call me 555-123-4567\", 16, \"Phone - should block\"),\n",
" (\"I'm at 123 Main Street\", 14, \"Address - should block\"),\n",
" (\"My credit card is 4111-1111-1111-1111\", 15, \"Credit Card - should block\"),\n",
" (\"Follow my instagram @cool\", 10, \"Social <13 - should block\"),\n",
" (\"Follow my instagram @cool\", 15, \"Social 13+ - should ALLOW\"),\n",
" (\"DM me on snapchat, it's secret\", 15, \"Grooming - should block\"),\n",
" (\"Check my tiktok\", 16, \"Social 16+ - should ALLOW\"),\n",
"]\n",
"\n",
"for text, age, note in pii_tests:\n",
" result = check_content(text, age)\n",
" status = \"✅\" if result['allowed'] else \"❌\"\n",
" print(f\"{status} Age {age}: {text[:45]}\")\n",
" print(f\" → {result['reason']}\")\n",
" if result.get('pii'):\n",
" print(f\" PII: {result['pii']}\")\n",
" print(f\" Note: {note}\")\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 8. Speed Test - Single Inference\n",
"test_text = \"damn that's crazy\"\n",
"\n",
"# Warm up\n",
"_ = check_content(test_text, 15)\n",
"\n",
"# Time single inference\n",
"times = []\n",
"for _ in range(100):\n",
" start = time.perf_counter()\n",
" result = check_content(test_text, 15)\n",
" end = time.perf_counter()\n",
" times.append((end - start) * 1000)\n",
"\n",
"avg_time = sum(times) / len(times)\n",
"print(f\"📊 Single Inference Speed (100 runs, with PII check)\")\n",
"print(f\" Average: {avg_time:.3f} ms\")\n",
"print(f\" Min: {min(times):.3f} ms\")\n",
"print(f\" Max: {max(times):.3f} ms\")\n",
"print(f\" Throughput: {1000/avg_time:.1f} inferences/second\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 9. Dual-Mode Content Test\n",
"test_cases = [\n",
" (\"that was a great game\", 10),\n",
" (\"that was a great game\", 15),\n",
" (\"shit that sucks\", 10),\n",
" (\"shit that sucks\", 15),\n",
" (\"you're a piece of shit\", 15),\n",
" (\"kill yourself\", 12),\n",
" (\"damn that's crazy\", 10),\n",
"]\n",
"\n",
"print(\"📋 Dual-Mode Content Results\\n\")\n",
"print(f\"{'Text':<30} {'Age':<6} {'Status':<10} {'Reason':<30}\")\n",
"print(\"-\" * 80)\n",
"\n",
"for text, age in test_cases:\n",
" result = check_content(text, age)\n",
" status = \"✅ ALLOW\" if result['allowed'] else \"❌ BLOCK\"\n",
" print(f\"{text:<30} {age:<6} {status:<10} {result['reason'][:28]:<30}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 10. Batch Processing Speed Test\n",
"batch_texts = [\n",
" \"that was a great game\",\n",
" \"shit that sucks\",\n",
" \"you're awesome\",\n",
" \"My email is test@test.com\",\n",
" \"Follow me on instagram\",\n",
" \"kill yourself\",\n",
" \"nice work\",\n",
" \"Check my tiktok\",\n",
"] * 50 # 400 texts\n",
"\n",
"ages = [15] * len(batch_texts)\n",
"\n",
"print(f\"Processing batch of {len(batch_texts)} texts...\")\n",
"start = time.perf_counter()\n",
"results = [check_content(t, a) for t, a in zip(batch_texts, ages)]\n",
"end = time.perf_counter()\n",
"\n",
"total_time = (end - start) * 1000\n",
"print(f\"\\n📊 Batch Results\")\n",
"print(f\" Total time: {total_time:.1f} ms\")\n",
"print(f\" Average: {total_time/len(batch_texts):.3f} ms/text\")\n",
"print(f\" Throughput: {len(batch_texts)/(total_time/1000):.1f} texts/sec\")\n",
"\n",
"allowed = sum(1 for r in results if r['allowed'])\n",
"blocked = len(results) - allowed\n",
"print(f\"\\n Allowed: {allowed} | Blocked: {blocked}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title 11. Summary\n",
"print(\"📊 moderat Summary\")\n",
"print(\"=\"*60)\n",
"print(\"\")\n",
"print(\"✅ Content Moderation:\")\n",
"print(\" - 6 categories (Safe, Harassment, Swearing, Hate, Spam)\")\n",
"print(\" - Dual-mode: <13 strict, 13+ laxed\")\n",
"print(\"\")\n",
"print(\"✅ PII Detection:\")\n",
"print(\" - Email, Phone, Address, Credit Card (all ages blocked)\")\n",
"print(\" - Social Media: <13 blocked, 13+ allowed\")\n",
"print(\" - Grooming detection for 13+\")\n",
"print(\"\")\n",
"print(\"📈 Speed:\")\n",
"print(\" - ~3-7ms per inference (with PII)\")\n",
"print(\" - ~200-500 texts/sec batch\")\n",
"print(\"\")\n",
"print(\"🔗 https://huggingface.co/darwinkernelpanic/moderat\")"
]
}
]
}
|