FAQ.md

Frequently Asked Questions

Which tasks are supported?

Image and text classification via Transformers (e.g., NSFW detection, sentiment/toxicity analysis). More tasks can be added over time.

Does it need a GPU?

No. CPU is fine for small models. If your framework has CUDA installed, it will automatically use GPU acceleration.

How are dependencies handled?

If something is missing (e.g., torch, transformers, Pillow), Moderators can auto-install via uv or pip unless you disable it.

To disable auto-installation:

export MODERATORS_DISABLE_AUTO_INSTALL=1

For manual dependency control:

pip install "moderators[transformers]"

Can I run offline?

Yes. Use --local-files-only in the CLI or local_files_only=True in Python after you have the model cached.

CLI:

moderators model-id input.jpg --local-files-only

Python:

moderator = AutoModerator.from_pretrained("model-id", local_files_only=True)

What does "normalized output" mean?

Regardless of the underlying pipeline, you always get the same result schema (PredictionResult with classifications/detections/raw_output), so your application code stays simple and consistent across different models.

Can I use my own custom models?

Yes! As long as your model has a config.json file and is compatible with Transformers, you can use it with Moderators. Just point to the model directory or Hugging Face model ID.

How do I contribute or request features?

Check out the GitHub repository to open issues or submit pull requests. Feature requests and contributions are welcome!