Use GLiNER for NER annotation
The GLiNER model is a BERT family model for generalist NER. We download the model from HuggingFace, but the original
model is available on GitHub.
Running with Docker (recommended)
- Start Machine Learning backend on
http://localhost:9090
with prebuilt image:
docker-compose up
- Validate that backend is running
$ curl http://localhost:9090/
{"status":"UP"}
- Create a project in Label Studio. Then from the Model page in the project settings, connect the model. The default URL is
http://localhost:9090
.
Building from source (advanced)
To build the ML backend from source, you have to clone the repository and build the Docker image:
docker-compose build
Running without Docker (advanced)
To run the ML backend without Docker, you have to clone the repository and install all dependencies using pip:
python -m venv ml-backend
source ml-backend/bin/activate
pip install -r requirements.txt
Then you can start the ML backend:
label-studio-ml start ./dir_with_your_model
Configuration
Parameters can be set in docker-compose.yml
before running the container.
The following common parameters are available:
BASIC_AUTH_USER
- Specify the basic auth user for the model server.BASIC_AUTH_PASS
- Specify the basic auth password for the model server.LOG_LEVEL
- Set the log level for the model server.WORKERS
- Specify the number of workers for the model server.THREADS
- Specify the number of threads for the model server.LABEL_STUDIO_URL
- Specify the URL of your Label Studio instance. Note that this might need to behttp://host.docker.internal:8080
if you are running Label Studio on another Docker container.LABEL_STUDIO_API_KEY
- Specify the API key for authenticating your Label Studio instance. You can find this by logging into Label Studio and and going to the Account & Settings page.