Gliner Medium V2.5

🚀 GLiNER

GLiNER is a Named Entity Recognition (NER) model that can identify any entity type, offering an alternative to traditional NER models and LLMs.

🚀 Quick Start

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.

✨ Features

• Capable of identifying any entity type.
• Offers an alternative to traditional NER models and LLMs.

📦 Installation

To use this model, you must install the GLiNER Python library:

!pip install gliner -U

💻 Usage Examples

Basic Usage

from gliner import GLiNER

model = GLiNER.from_pretrained("gliner-community/gliner_medium-v2.5", load_tokenizer=True)

text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; 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 = ["person", "award", "date", "competitions", "teams"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

Advanced Usage

# The above example shows the basic usage of GLiNER. For more complex scenarios, you can adjust the input text and labels according to your needs.

📚 Documentation

Links

• Paper: https://arxiv.org/abs/2311.08526
• Repository: https://github.com/urchade/GLiNER

Named Entity Recognition benchmark result

Below is a comparison of results between previous versions of the model and the current one:

Available models

🔧 Technical Details

GLiNER uses a bidirectional transformer encoder (BERT-like) to identify any entity type.

📄 License

This project is licensed under the Apache-2.0 license.

📖 Model Authors

The model authors are:

• Urchade Zaratiana
• Ihor Stepanov
• Nadi Tomeh
• Pierre Holat
• Thierry Charnois

📚 Citation

@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}
}