---
title: LS GLiNER Backend
emoji: 🪄
colorFrom: pink
colorTo: purple
sdk: docker
app_port: 9090
pinned: false
license: apache-2.0
short_description: GLiNER zero-shot NER as a Label Studio ML backend
---

# LS GLiNER Backend (Hugging Face Spaces)

This Space wraps HumanSignal's [`gliner` ML backend example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/gliner) as a Hugging Face Space. GLiNER is a zero-shot NER model — it accepts arbitrary user-defined labels at inference time, so it can predict any label your LS project's config defines without retraining.

Default model: `urchade/gliner_medium-v2.1` (~750MB). Override via `GLINER_MODEL_NAME` env var.

**Patches from the upstream example (minimal):**
- Added Spaces SDK frontmatter at the top of this README.
- Removed `docker-compose.yml` (not used on Spaces).

Connect from Label Studio: set the ML backend URL to `https://davanstrien-ls-gliner-backend.hf.space`.

---

<!-- Original upstream README below -->

<!--
---
title: Use GLiNER for NER annotation
type: guide
tier: all
order: 37
hide_menu: true
hide_frontmatter_title: true
meta_title: Use GLiNER for NER annotation
meta_description: Tutorial on how to use GLiNER with your Label Studio project to complete NER tasks
categories:
    - Natural Language Processing
    - Named Entity Recognition
    - GLiNER
    - BERT
    - Hugging Face
image: "/guide/ml_tutorials/gliner.png"
---
-->

# 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](https://github.com/urchade/GLiNER).

## Before you begin

Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 

This tutorial uses the [`gliner` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/gliner). 


## Running with Docker (recommended)

1. Start Machine Learning backend on `http://localhost:9090` with prebuilt image:

```bash
docker-compose up
```

2. Validate that backend is running

```bash
$ curl http://localhost:9090/
{"status":"UP"}
```

3. Create a project in Label Studio. Then from the **Model** page in the project settings, [connect the model](https://labelstud.io/guide/ml#Connect-the-model-to-Label-Studio). 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:

```bash
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:

```bash
python -m venv ml-backend
source ml-backend/bin/activate
pip install -r requirements.txt
```

Then you can start the ML backend:

```bash
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 be `http://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](https://labelstud.io/guide/user_account#Access-token). 

## A Note on Model Training 

If you plan to use a webhook to train this model on "Start Training", note that you do 
not need to configure a separate webhook. Instead, go to the three dots next to your model
on the Model tab in your project settings and click "start training". 

Additionally, note that this container has been set for a **VERY SMALL** demo set, with only 1
non-eval sample (we expect the first 10 data samples to be for evaluation.)

If you're working with a larger dataset, be sure to: 
1. update num_steps and batch size to the number of training steps you want and the batch size that works for your dataset. 
2. change the uploaded model after training (line 239 of `model.py`) to the highest checkpoint that you have.