LocalMod

Fully offline content moderation API — Free, self-hosted, and private. Your data never leaves your infrastructure.

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Benchmark Results

Toxicity Detection

Benchmarked using CHI 2025 "Lost in Moderation" methodology (HateXplain, Civil Comments, SBIC datasets):

System	Balanced Accuracy	Type
OpenAI Moderation API	0.83	Commercial
Azure Content Moderator	0.81	Commercial
LocalMod	0.75 ⭐	Open Source
Amazon Comprehend	0.74	Commercial
Perspective API	0.62	Commercial

Spam Detection

System	Balanced Accuracy	Dataset
LocalMod	0.998	UCI SMS Spam Collection

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Installation

git clone https://github.com/KOKOSde/localmod.git
cd localmod
pip install -e .

# Download ML models (~3.5GB - includes image model)
python scripts/download_models.py

Quick Start

# Run demo
python examples/demo.py

Python Usage

from localmod import SafetyPipeline

pipeline = SafetyPipeline()
report = pipeline.analyze("Check this text for safety issues")

print(f"Flagged: {report.flagged}")
print(f"Severity: {report.severity}")

API Server

localmod serve --port 8000

# Test
curl -X POST http://localhost:8000/analyze \
  -H "Content-Type: application/json" \
  -d '{"text": "Hello world", "classifiers": ["toxicity", "pii"]}'

Docker

docker build -f docker/Dockerfile -t localmod:latest .
docker run -p 8000:8000 localmod:latest

Discord Bot 🆕

# Install Discord dependency
pip install -e ".[discord]"

# Set your bot token
export DISCORD_BOT_TOKEN=your_token_here

# Run the bot
python examples/discord_bot.py

Features: Real-time text & image moderation, auto-delete, timeout, logging.

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Classifiers

Text Moderation

Classifier	Detects	Model
PII	Emails, phones, SSNs, credit cards	Regex + Validation
Toxicity	Hate speech, harassment, threats	Weighted Ensemble (4 models)
Prompt Injection	LLM jailbreaks, instruction override	DeBERTa
Spam	Promotional content, scams	RoBERTa
NSFW Text	Sexual content, adult themes	NSFW Classifier

Image Moderation 🆕

Classifier	Detects	Model
NSFW Image	Explicit/adult images	Falconsai/nsfw_image_detection (ViT)

71M+ downloads on HuggingFace • Apache 2.0 license

Toxicity Ensemble

Model	Weight	Purpose
unitary/toxic-bert	50%	Multi-label toxicity
Hate-speech-CNERG/dehatebert-mono-english	20%	Hate speech
s-nlp/roberta_toxicity_classifier	15%	Toxicity
facebook/roberta-hate-speech-dynabench-r4-target	15%	Adversarial robustness

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API Endpoints

Text Moderation

Endpoint	Method	Description
/analyze	POST	Analyze single text
/analyze/batch	POST	Analyze multiple texts
/redact	POST	Redact PII from text

Image Moderation 🆕

Endpoint	Method	Description
/analyze/image	POST	Analyze image from URL
/analyze/image/upload	POST	Analyze uploaded image file

System

Endpoint	Method	Description
/health	GET	Health check
/classifiers	GET	List text classifiers
/classifiers/image	GET	List image classifiers

Example: Text Analysis

POST /analyze
{
  "text": "You are an idiot!",
  "classifiers": ["toxicity"]
}

Response:
{
  "flagged": true,
  "results": [{"classifier": "toxicity", "flagged": true, "confidence": 0.72, "severity": "high"}],
  "processing_time_ms": 85.3
}

Example: Image Analysis 🆕

POST /analyze/image
{
  "image_url": "https://example.com/image.jpg"
}

Response:
{
  "flagged": false,
  "results": [{"classifier": "nsfw_image", "flagged": false, "confidence": 0.02, "severity": "none"}],
  "processing_time_ms": 120.5
}

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Offline Mode

# Download models once
python scripts/download_models.py --model-dir /path/to/models

# Run offline
export LOCALMOD_MODEL_DIR=/path/to/models
export LOCALMOD_OFFLINE=1
localmod serve

Docker:

docker run -p 8000:8000 \
  -v /path/to/models:/models \
  -e LOCALMOD_MODEL_DIR=/models \
  -e LOCALMOD_OFFLINE=1 \
  localmod:latest

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Configuration

Variable	Default	Description
LOCALMOD_MODEL_DIR	~/.cache/localmod/models	Model directory
LOCALMOD_OFFLINE	false	Force offline mode
LOCALMOD_DEVICE	auto	cpu, cuda, or auto

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Performance

Classifier	CPU Latency	GPU Latency	Memory
PII (regex)	<1ms	<1ms	Minimal
Single ML model (text)	~50-200ms	~10-30ms	~1GB
Toxicity ensemble (4 models)	~200-500ms	~30-80ms	~3GB
NSFW Image (ViT)	~100-300ms	~20-50ms	~500MB

Performance varies by hardware. GPU recommended for production workloads.

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Development

pip install -e ".[dev]"
pytest tests/ -v

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License

MIT License — see LICENSE.

Acknowledgments

Models from HuggingFace. Benchmark methodology from CHI 2025 "Lost in Moderation" (Hartmann et al.).