GLiNER is a generalist and lightweight Named Entity Recognition (NER) model designed to extract any entity type from text. It offers a flexible and efficient alternative to traditional NER models with fixed entity sets and large, costly LLMs, making it suitable for resource-constrained environments.

How It Works

GLiNER leverages a bidirectional transformer encoder (BERT-like) architecture. Its key advantage lies in its ability to perform parallel entity extraction, unlike the sequential generation of LLMs. This approach allows it to efficiently identify arbitrary entity types specified via natural language labels, outperforming LLMs in zero-shot evaluations on various NER benchmarks.

Quick Start & Requirements

• Install via pip: !pip install gliner
• Requires Python.
• Example notebooks are available for finetuning, ONNX conversion, and synthetic data generation.
• Demo available at 🤗 Demo.

Highlighted Details

• Capable of extracting any entity type specified by labels.
• Outperforms ChatGPT and fine-tuned LLMs in zero-shot NER evaluations.
• Supports parallel entity extraction for efficiency.
• Offers various pre-trained models via 🤗 Available models.

Maintenance & Community

• Maintained by Urchade Zaratiana and Ihor Stepanov.
• Model authors include Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois.
• Community support available via 📢 Discord.

Licensing & Compatibility

• The README does not explicitly state the license. However, the project is hosted on GitHub, implying a common open-source license. Further clarification on licensing is recommended for commercial use.

Limitations & Caveats

• The specific license is not detailed in the README, which may pose a concern for commercial applications.