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| 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. | |