# π GLiNER: Generalist and Lightweight Model for Named Entity Recognition
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
<p align="center">
<a href="https://pypi.org/project/gliner/" target="_blank">
<img alt="Python" src="https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54" />
<img alt="Version" src="https://img.shields.io/pypi/v/gliner?style=for-the-badge&color=3670A0">
</a>
</p>
<p align="center">
<a href="https://arxiv.org/abs/2311.08526">π Paper</a>
<span> β’ </span>
<a href="https://discord.gg/Y2yVxpSQnG">π’ Discord</a>
<span> β’ </span>
<a href="https://huggingface.co/spaces/urchade/gliner_mediumv2.1">π€ Demo</a>
<span> β’ </span>
<a href="https://huggingface.co/models?library=gliner&sort=trending">π€ Available models</a>
<span> β’ </span>
<a href="https://colab.research.google.com/drive/1mhalKWzmfSTqMnR0wQBZvt9-ktTsATHB?usp=sharing">
<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />
</a>
</p>
## Example Notebooks
Explore various examples including finetuning, ONNX conversion, and synthetic data generation.
- [Example Notebooks](https://github.com/urchade/GLiNER/tree/main/examples)
- Finetune on Colab [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/drive/1HNKd74cmfS9tGvWrKeIjSxBt01QQS7bq?usp=sharing)
## π Installation & Usage
### Installation
```bash
!pip install gliner
```
### Usage
After the installation of the GLiNER library, import the `GLiNER` class. Following this, you can load your chosen model with `GLiNER.from_pretrained` and utilize `predict_entities` to discern entities within your text.
```python
from gliner import GLiNER
# Initialize GLiNER with the base model
model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")
# Sample text for entity prediction
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kΙΎiΚΛtjΙnu ΚΙΛnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""
# Labels for entity prediction
# Most GLiNER models should work best when entity types are in lower case or title case
labels = ["Person", "Award", "Date", "Competitions", "Teams"]
# Perform entity prediction
entities = model.predict_entities(text, labels, threshold=0.5)
# Display predicted entities and their labels
for entity in entities:
print(entity["text"], "=>", entity["label"])
```
#### Expected Output
```
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Al Nassr => teams
Portugal national team => teams
Ballon d'Or => award
UEFA Men's Player of the Year Awards => award
European Golden Shoes => award
UEFA Champions Leagues => competitions
UEFA European Championship => competitions
UEFA Nations League => competitions
European Championship => competitions
```
## π Maintainers
<div align="center">
<table>
<tr>
<td align="center">
<strong>Urchade Zaratiana</strong><br>
<em>PhD Student at LIPN</em><br>
<a href="https://www.linkedin.com/in/urchade-zaratiana/"><img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="LinkedIn" /></a>
</td>
<td align="center">
<strong>Ihor Stepanov</strong><br>
<em>Co-Founder at Knowledgator</em><br>
<a href="https://www.linkedin.com/in/ihor-stepanov/"><img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="LinkedIn" /></a>
</td>
</tr>
</table>
</div>
## π¨βπ» Model Authors
The model authors are:
* [Urchade Zaratiana](https://huggingface.co/urchade)
* Nadi Tomeh
* Pierre Holat
* Thierry Charnois
## π Citation
If you find GLiNER useful in your research, please consider citing our paper:
```bibtex
@misc{zaratiana2023gliner,
title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
year={2023},
eprint={2311.08526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Support and funding
This project has been supported and funded by **F.initiatives** and **Laboratoire Informatique de Paris Nord**.
F.initiatives has been an expert in public funding strategies for R&D, Innovation, and Investments (R&D&I) for over 20 years. With a team of more than 200 qualified consultants, F.initiatives guides its clients at every stage of developing their public funding strategy: from structuring their projects to submitting their aid application, while ensuring the translation of their industrial and technological challenges to public funders. Through its continuous commitment to excellence and integrity, F.initiatives relies on the synergy between methods and tools to offer tailored, high-quality, and secure support.
<p align="center">
<img src="logo/FI_COMPLET_CW.png" alt="FI Group" width="300"/>
</p>
We also extend our heartfelt gratitude to the open-source community for their invaluable contributions, which have been instrumental in the success of this project.
Raw data
{
"_id": null,
"home_page": null,
"name": "gliner",
"maintainer": "Urchade Zaratiana",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "named-entity-recognition, ner, data-science, natural-language-processing, artificial-intelligence, nlp, machine-learning, transformers",
"author": "Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/99/d9/048f75ac2bc98046562d68001c64837358135aba9a3df9d88321cfaf8d12/gliner-0.2.13.tar.gz",
"platform": null,
"description": "# \ud83d\udc51 GLiNER: Generalist and Lightweight Model for Named Entity Recognition\n\nGLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/gliner/\" target=\"_blank\">\n <img alt=\"Python\" src=\"https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54\" />\n <img alt=\"Version\" src=\"https://img.shields.io/pypi/v/gliner?style=for-the-badge&color=3670A0\">\n </a>\n</p>\n\n<p align=\"center\">\n <a href=\"https://arxiv.org/abs/2311.08526\">\ud83d\udcc4 Paper</a>\n <span> \u2022 </span>\n <a href=\"https://discord.gg/Y2yVxpSQnG\">\ud83d\udce2 Discord</a>\n <span> \u2022 </span>\n <a href=\"https://huggingface.co/spaces/urchade/gliner_mediumv2.1\">\ud83e\udd17 Demo</a>\n <span> \u2022 </span>\n <a href=\"https://huggingface.co/models?library=gliner&sort=trending\">\ud83e\udd17 Available models</a>\n <span> \u2022 </span>\n <a href=\"https://colab.research.google.com/drive/1mhalKWzmfSTqMnR0wQBZvt9-ktTsATHB?usp=sharing\">\n <img align=\"center\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" />\n </a>\n</p>\n\n## Example Notebooks\n\nExplore various examples including finetuning, ONNX conversion, and synthetic data generation. \n\n- [Example Notebooks](https://github.com/urchade/GLiNER/tree/main/examples)\n- Finetune on Colab [<img align=\"center\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" />](https://colab.research.google.com/drive/1HNKd74cmfS9tGvWrKeIjSxBt01QQS7bq?usp=sharing)\n## \ud83d\udee0 Installation & Usage\n\n### Installation\n```bash\n!pip install gliner\n```\n\n### Usage\nAfter the installation of the GLiNER library, import the `GLiNER` class. Following this, you can load your chosen model with `GLiNER.from_pretrained` and utilize `predict_entities` to discern entities within your text.\n\n```python\nfrom gliner import GLiNER\n\n# Initialize GLiNER with the base model\nmodel = GLiNER.from_pretrained(\"urchade/gliner_mediumv2.1\")\n\n# Sample text for entity prediction\ntext = \"\"\"\nCristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [k\u027ei\u0283\u02c8tj\u0250nu \u0281\u0254\u02c8naldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.\n\"\"\"\n\n# Labels for entity prediction\n# Most GLiNER models should work best when entity types are in lower case or title case\nlabels = [\"Person\", \"Award\", \"Date\", \"Competitions\", \"Teams\"]\n\n# Perform entity prediction\nentities = model.predict_entities(text, labels, threshold=0.5)\n\n# Display predicted entities and their labels\nfor entity in entities:\n print(entity[\"text\"], \"=>\", entity[\"label\"])\n```\n\n#### Expected Output\n\n```\nCristiano Ronaldo dos Santos Aveiro => person\n5 February 1985 => date\nAl Nassr => teams\nPortugal national team => teams\nBallon d'Or => award\nUEFA Men's Player of the Year Awards => award\nEuropean Golden Shoes => award\nUEFA Champions Leagues => competitions\nUEFA European Championship => competitions\nUEFA Nations League => competitions\nEuropean Championship => competitions\n```\n## \ud83c\udf1f Maintainers\n\n<div align=\"center\">\n <table>\n <tr>\n <td align=\"center\">\n <strong>Urchade Zaratiana</strong><br>\n <em>PhD Student at LIPN</em><br>\n <a href=\"https://www.linkedin.com/in/urchade-zaratiana/\"><img src=\"https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white\" alt=\"LinkedIn\" /></a>\n </td>\n <td align=\"center\">\n <strong>Ihor Stepanov</strong><br>\n <em>Co-Founder at Knowledgator</em><br>\n <a href=\"https://www.linkedin.com/in/ihor-stepanov/\"><img src=\"https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white\" alt=\"LinkedIn\" /></a>\n </td>\n </tr>\n </table>\n</div>\n\n## \ud83d\udc68\u200d\ud83d\udcbb Model Authors\nThe model authors are:\n* [Urchade Zaratiana](https://huggingface.co/urchade)\n* Nadi Tomeh\n* Pierre Holat\n* Thierry Charnois\n\n## \ud83d\udcda Citation\n\nIf you find GLiNER useful in your research, please consider citing our paper:\n\n```bibtex\n@misc{zaratiana2023gliner,\n title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, \n author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},\n year={2023},\n eprint={2311.08526},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n## Support and funding\n\nThis project has been supported and funded by **F.initiatives** and **Laboratoire Informatique de Paris Nord**.\n\nF.initiatives has been an expert in public funding strategies for R&D, Innovation, and Investments (R&D&I) for over 20 years. With a team of more than 200 qualified consultants, F.initiatives guides its clients at every stage of developing their public funding strategy: from structuring their projects to submitting their aid application, while ensuring the translation of their industrial and technological challenges to public funders. Through its continuous commitment to excellence and integrity, F.initiatives relies on the synergy between methods and tools to offer tailored, high-quality, and secure support.\n\n<p align=\"center\">\n <img src=\"logo/FI_COMPLET_CW.png\" alt=\"FI Group\" width=\"300\"/>\n</p>\n\nWe also extend our heartfelt gratitude to the open-source community for their invaluable contributions, which have been instrumental in the success of this project.\n\n\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "Generalist model for NER (Extract any entity types from texts)",
"version": "0.2.13",
"project_urls": {
"Homepage": "https://github.com/urchade/GLiNER"
},
"split_keywords": [
"named-entity-recognition",
" ner",
" data-science",
" natural-language-processing",
" artificial-intelligence",
" nlp",
" machine-learning",
" transformers"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "2baa9f08dddffac7f85f61a2a180a64ec76a88dda082cc7de61c6fd2f7fb449b",
"md5": "aae86f543c7630c258dbcc84baeac2b8",
"sha256": "a8798aef78b8a7322dc4f785c15d58c526f86698ec95feb892b500b5be507437"
},
"downloads": -1,
"filename": "gliner-0.2.13-py3-none-any.whl",
"has_sig": false,
"md5_digest": "aae86f543c7630c258dbcc84baeac2b8",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 47697,
"upload_time": "2024-09-21T04:16:48",
"upload_time_iso_8601": "2024-09-21T04:16:48.958926Z",
"url": "https://files.pythonhosted.org/packages/2b/aa/9f08dddffac7f85f61a2a180a64ec76a88dda082cc7de61c6fd2f7fb449b/gliner-0.2.13-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "99d9048f75ac2bc98046562d68001c64837358135aba9a3df9d88321cfaf8d12",
"md5": "73e41abbf1b3a3cebc47bbebabdd695c",
"sha256": "0afa0aa82f7f31dcc1befb667cb69d1a9760173078d770517381bf7c3c6b378f"
},
"downloads": -1,
"filename": "gliner-0.2.13.tar.gz",
"has_sig": false,
"md5_digest": "73e41abbf1b3a3cebc47bbebabdd695c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 44570,
"upload_time": "2024-09-21T04:16:51",
"upload_time_iso_8601": "2024-09-21T04:16:51.280150Z",
"url": "https://files.pythonhosted.org/packages/99/d9/048f75ac2bc98046562d68001c64837358135aba9a3df9d88321cfaf8d12/gliner-0.2.13.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-21 04:16:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "urchade",
"github_project": "GLiNER",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [],
"lcname": "gliner"
}
JSON
