spaCy powered Label Studio ML backend

spaCy integration for Label Studio. Created as an Open Source alternative to Prodigy.

Benefits:

• Speed up annotation of data with integrated predictions
• Quickly iterate on your spaCy models

Demo video

Usage

1. Clone this repo

2. Install requirements

pip install -r requirements.txt

3. Initialize a new backend

label-studio-ml init my_ml_backend

4. In the my_ml_backend directory, add your spaCy config.cfg file. You can optionally add a model-best folder from a pre-trained model, to get started with predictions straight away.

5. Start the backend and add the URL to your Label Studio project settings.

label-studio-ml start my_ml_backend

6. As you train new models, they will appear in a checkpoints directory. The latest checkpoint will be symlinked to latest-model.