---
title: "GLiNER"
type: "tool"
slug: "urchade-gliner"
canonical_url: "https://www.graphcanon.com/tools/urchade-gliner"
github_url: "https://github.com/urchade/GLiNER"
homepage_url: "https://urchade.github.io/GLiNER"
stars: 3373
forks: 288
primary_language: "Python"
license: "Apache-2.0"
categories: ["llm-frameworks", "developer-tools"]
tags: ["information-extraction", "prompt-tuning", "large-language-models", "natural-language-processing", "named-entity-recognition"]
updated_at: "2026-07-07T18:42:45.255732+00:00"
---

# GLiNER

> Generalist and Lightweight Model for Named Entity Recognition

A Python-based tool offering zero-shot named entity recognition, relation extraction, PII detection, information extraction, and token classification.

## Facts

- Repository: https://github.com/urchade/GLiNER
- Homepage: https://urchade.github.io/GLiNER
- Stars: 3,373 · Forks: 288 · Open issues: 96 · Watchers: 17
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-06T08:28:40+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [Developer Tools](/categories/developer-tools.md)

## Tags

information-extraction, prompt-tuning, large language models, natural-language-processing, named-entity-recognition

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## README (excerpt)

```text
<div align="center">
  <a href="https://pioneer.ai/gliner" target="_blank" rel="noopener noreferrer">
    <img src="image/GitHub.png" alt="Pioneer AI - Fine-tune GLiNER with a single prompt" width="100%"/>
  </a>
</div>

> [!IMPORTANT]
> **🚀 GLiNER2 is Now Available from [Fastino Labs](https://github.com/fastino-ai)!** A unified multi-task model for NER, Text Classification & Structured Data Extraction. Check out [fastino-ai/GLiNER2 →](https://github.com/fastino-ai/GLiNER2)


<div align="center">

# GLiNER: Generalist and Lightweight Model for Named Entity Recognition

**Zero-shot NER | Relation Extraction | PII Detection | Information Extraction | Token Classification**

<div>
    
    <a href="https://urchade.github.io/GLiNER"><img src="https://img.shields.io/badge/Docs-GLiNER-blue" alt="GLiNER Documentation"></a>
    <a href="https://arxiv.org/abs/2311.08526"><img src="https://img.shields.io/badge/arXiv-2311.08526-b31b1b.svg" alt="GLiNER Paper"></a>
    <a href="https://colab.research.google.com/drive/1mhalKWzmfSTqMnR0wQBZvt9-ktTsATHB?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open GLiNER In Colab"></a>
    <a href="https://github.com/urchade/GLiNER/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/urchade/GLiNER?color=blue"></a>
    <br>
    
    <a href="https://discord.gg/x7hQsjX2Kk"><img alt="GLiNER Community Discord" src="https://img.shields.io/badge/Discord-GLiNER%20Community-5865F2?logo=discord&logoColor=white"></a>
    <a href="https://www.reddit.com/r/GLiNER/"><img src="https://img.shields.io/badge/Reddit-r%2FGLiNER-FF4500?logo=reddit&logoColor=white" alt="Reddit r/GLiNER"></a>
    <a href="https://huggingface.co/spaces/urchade/gliner_mediumv2.1"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg" alt="Open GLiNER In HF Spaces"></a>
    <a href="https://huggingface.co/models?library=gliner&sort=trending"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow" alt="HuggingFace Models"></a>
    <br>
    
    <a href="https://clickpy.clickhouse.com/dashboard/gliner"><img src="https://static.pepy.tech/badge/gliner" alt="GLiNER Downloads"></a>
    <a href="https://github.com/urchade/GLiNER"><img alt="GLiNER GitHub stars" src="https://img.shields.io/github/stars/urchade/GLiNER?style=social"></a>
</div>
<br>
</div>


<div align="center">
  <img src="assets/banner.png" alt="GLiNER Banner" width="100%">
</div>

GLiNER is a framework for training and deploying small Named Entity Recognition (NER) models with zero-shot capabilities. In addition to traditional NER, it also supports joint entity and relation extraction, as well as multi-task token classification. GLiNER is fine-tunable, optimized to run on CPUs and consumer hardware, and has performance competitive with LLMs several times its size, like ChatGPT and UniNER.

Other tasks such as text classification, entity linking, and schema extraction are supported through projects in the [Ecosystem](#ecosystem).

## Why GLiNER?

<table>
<tr>
<td width="33%" align="center">
<b>Zero-shot Recognition</b>
<p>Extract any entity type — no labeled data or task-specific training required</p>
</td>
<td width="33%" align="center">
<b>Runs Anywhere</b>
<p>CPU, INT8 quantization, <code>torch.compile</code>, ONNX export — deploy on any hardware</p>
</td>
<td width="33%" align="center">
<b>Millions of Labels</b>
<p>Bi-encoder pre-computes label embeddings, scaling to 100+ entity types without degradation</p>
</td>
</tr>
<tr>
<td width="33%" align="center">
<b>NER + Relations</b>
<p>Build knowledge graphs in a single pass with the joint RelEx architecture</p>
</td>
<td width="33%" align="center">
<b>PII Detection</b>
<p>State-of-the-art multilingual PII models covering major entity types across 100+ languages</p>
</td>
<td width="33%" align="center">
<b>Fine-Tune in Minutes</b>
<p>Few-shot learning on small datasets — bring your own labels and
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/urchade-gliner`](/api/graphcanon/tools/urchade-gliner)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_