Gliner Medium V2.1

🚀 GLiNER: A Generalist Named Entity Recognition Model

GLiNER is a Named Entity Recognition (NER) model. It uses a bidirectional transformer encoder (BERT - like) to identify any entity type. It's a practical alternative to traditional NER models with predefined entities and large, costly LLMs in resource - constrained scenarios.

🚀 Quick Start

GLiNER offers a straightforward way to perform named entity recognition. First, you need to install the library and then you can start using it to identify entities in text.

✨ Features

• Flexible Entity Identification: Capable of identifying any entity type, unlike traditional NER models limited to predefined entities.
• Resource - Friendly: A better choice than Large Language Models (LLMs) in resource - constrained scenarios.

📦 Installation

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

!pip install gliner

💻 Usage Examples

Basic Usage

Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using GLiNER.from_pretrained and predict entities with predict_entities.

from gliner import GLiNER

model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")

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"])

The output will be:

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
Champions League => competitions
European Championship => competitions

📚 Documentation

Links

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

Available models

Named Entity Recognition benchmark result

🔧 Technical Details

GLiNER uses a bidirectional transformer encoder similar to BERT, enabling it to capture context from both directions in the text. This architecture allows the model to identify various entity types effectively.

📄 License

The model is licensed under the Apache - 2.0 license.

Model Authors

The model authors are:

• Urchade Zaratiana
• 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}
}