Creating an Uncensored Content ModeratorCreatinganUncensoredContentModerator

First, let's install the necessary dependencies:

Install Python and Git:

• Download Python from python.org
• Download Git from git-scm.com

Install Docker (Optional):

• Download Docker Desktop from docker.com

python --version git --version docker --version

Clone the Content Moderator repository:

git clone https://github.com/privatai/content-moderator.git cd content-moderator

Create a virtual environment and install dependencies:

python -m venv venv venv\Scripts\activate pip install -r requirements.txt

Create a policy.json file to define content categories:

{
  "categories": [
    {
      "name": "adult_content",
      "description": "Content containing explicit adult material",
      "action": "flag",
      "threshold": 0.85
    },
    {
      "name": "harassment",
      "description": "Content containing personal attacks or bullying",
      "action": "reject",
      "threshold": 0.75
    },
    {
      "name": "hate_speech",
      "description": "Content targeting specific protected groups",
      "action": "reject",
      "threshold": 0.80
    },
    {
      "name": "violence",
      "description": "Content depicting or promoting violence",
      "action": "flag",
      "threshold": 0.85
    },
    {
      "name": "self_harm",
      "description": "Content related to self-harm or suicide",
      "action": "escalate",
      "threshold": 0.70
    }
  ]
}

Customize this template based on your requirements:

• name: Category identifier
• description: Detailed explanation of the category
• action: What to do when content matches (flag, reject, escalate)
• threshold: Confidence level required (0.0-1.0)

Edit the config.json file to set system-wide behavior:

{
  "system": {
    "log_all_content": true,
    "human_review_queue": true,
    "auto_update_models": false
  },
  "model": {
    "name": "roberta-base-openai-detector",
    "device": "cuda",
    "batch_size": 16
  },
  "api": {
    "port": 5000,
    "rate_limit": 100,
    "require_auth": true
  }
}

Key configuration options:

• log_all_content: Whether to keep records of all processed content
• human_review_queue: Enable queue for uncertain classifications
• auto_update_models: Automatically update AI models
• device: Use "cuda" for GPU or "cpu" for CPU-only mode

Use the download script to get the appropriate model:

python download_models.py

This script will download a pre-trained model that can be fine-tuned for your specific moderation needs.

For better performance, create a dataset with examples specific to your needs:

Create a training dataset:

[
  {
    "text": "Example text that should be flagged in your adult_content category",
    "labels": ["adult_content"]
  },
  {
    "text": "Example text that should be flagged as harassment",
    "labels": ["harassment"]
  },
  {
    "text": "Example text that contains hate speech and violence",
    "labels": ["hate_speech", "violence"]
  },
  {
    "text": "Example text that is completely acceptable",
    "labels": []
  }
]

Save this as training_data.json in the data directory.

Fine-tune the model on your custom dataset:

python finetune.py --data data/training_data.json --epochs 3 --batch_size 8

This process adapts the base model to your specific content categories and policy needs.

Launch the moderation server:

python run_server.py

This starts the server on localhost, port 5000 (or as configured in config.json).

Use the test client to validate your moderation system:

python test_client.py --text "Sample text to test moderation"

For batch testing with a file:

python test_client.py --file test_samples.txt

How to interpret moderation results:

{
  "text": "Sample text that was analyzed",
  "categories": {
    "adult_content": {
      "score": 0.12,
      "action": "pass"
    },
    "harassment": {
      "score": 0.82,
      "action": "reject"
    },
    "hate_speech": {
      "score": 0.05,
      "action": "pass"
    },
    "violence": {
      "score": 0.22,
      "action": "pass"
    },
    "self_harm": {
      "score": 0.03,
      "action": "pass"
    }
  },
  "final_action": "reject",
  "reason": "Content violates policy: harassment"
}

Key elements in results:

• score: Confidence level for each category (0.0-1.0)
• action: Action for each category based on threshold
• final_action: Overall decision (most restrictive wins)
• reason: Explanation for the decision

Integrate with your platform using the REST API:

Example API request:

curl -X POST "http://localhost:5000/api/moderate" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your_api_key" \
  -d '{"text": "Content to be moderated"}'

Python integration example:

import requests
import json

api_url = "http://localhost:5000/api/moderate"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer your_api_key"
}

data = {
    "text": "Content to be moderated"
}

response = requests.post(api_url, headers=headers, json=data)
result = response.json()

if result["final_action"] == "pass":
    # Content is allowed
    print("Content approved")
else:
    # Content is flagged or rejected
    print(f"Content {result['final_action']}: {result['reason']}")

Set up the human review dashboard:

python run_dashboard.py

This launches a web interface at http://localhost:8080 for human reviewers to handle:

• Content that was flagged for review
• Edge cases with uncertain classification
• User appeals of moderation decisions

For production deployment, use Docker:

docker build -t content-moderator . docker run -p 5000:5000 -p 8080:8080 content-moderator

Security considerations:

• Use strong API keys for authentication
• Limit API access to trusted IPs
• Implement rate limiting to prevent abuse
• Regularly back up moderation logs and decisions