---
title: Ner Annotation
emoji: 👀
colorFrom: pink
colorTo: green
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
short_description: the ui for annotation ner for healthcare
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

# NER Annotation Tool

A powerful tool for annotating text with named entities using GLiNER models. This tool provides both automatic annotation using pre-trained models and a manual annotation interface for reviewing and correcting the results.

## Features

- Automatic NER annotation using GLiNER models
- Support for multiple pre-trained models
- Interactive dataset viewer and editor
- Export/import functionality for annotated data
- Integration with Hugging Face Hub for dataset sharing
- Support for various file formats (JSON, CoNLL, TXT)

## Installation

1. Clone the repository:
```bash
git clone https://github.com/yourusername/ner-annotation.git
cd ner-annotation
```

2. Create and activate a virtual environment:
```bash
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
```

3. Install the package:
```bash
pip install -e .
```

## Usage

1. Start the application:
```bash
python -m ner_annotation.app
```

2. The application will open in your default web browser with two main tabs:

   - **Auto Annotation**: Upload text files and automatically annotate them using GLiNER models
   - **Dataset Viewer**: Review, edit, and validate annotated data

### Auto Annotation

1. Upload a text file (one sentence per line)
2. Select a GLiNER model
3. Enter the entity labels to detect (comma-separated)
4. Adjust the confidence threshold
5. Optionally add a prompt
6. Click "Annotate Data"

### Dataset Viewer

1. Load a dataset (local or from Hugging Face)
2. Navigate through examples using the slider or buttons
3. Edit annotations as needed
4. Validate examples
5. Save the dataset locally or to Hugging Face Hub

## Configuration

Create a `.env` file in the project root with your Hugging Face token:
```
HUGGINGFACE_ACCESS_TOKEN=your_token_here
```

## Available Models

- `BookingCare/gliner-multi-healthcare`
- `knowledgator/gliner-multitask-large-v0.5`
- `knowledgator/gliner-multitask-base-v0.5`

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.