Gliner Poly Small V1.0

🚀 GLiNER

GLiNER is a Named Entity Recognition (NER) model. It can identify any entity type using bidirectional transformer encoders (BERT-like). It offers a practical alternative to traditional NER models and Large Language Models (LLMs), providing a cost - effective solution for resource - constrained scenarios.

🚀 Quick Start

GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using bidirectional transformer encoders (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.

This particular version utilizes a bi - encoder architecture with post - fusion. The textual encoder is [DeBERTa v3 small](microsoft/deberta - v3 - small), and the entity label encoder is a sentence transformer - [BGE - small - en](https://huggingface.co/BAAI/bge - small - en - v1.5).

Such an architecture brings several advantages over the uni - encoder GLiNER:

• An unlimited amount of entities can be recognized at a single time;
• Faster inference if entity embeddings are preprocessed;
• Better generalization to unseen entities.

The post - fusion strategy brings advantages over the classical bi - encoder, enabling better inter - label understanding.

✨ Features

• Flexible Entity Recognition: Capable of identifying any entity type, not limited to predefined ones.
• Cost - Effective: A practical alternative to LLMs for resource - constrained scenarios.
• Bi - encoder Architecture: With post - fusion, offering multiple advantages such as unlimited entity recognition at once, faster inference with preprocessed embeddings, and better generalization.

📦 Installation

Install or update the gliner package:

pip install gliner -U

💻 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("knowledgator/gliner - poly - small - v1.0")

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, threshold = 0.25)

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

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

Advanced Usage

If you have a large amount of entities and want to pre - embed them, please refer to the following code snippet:

labels = ["your entities"]
texts = ["your texts"]

entity_embeddings = model.encode_labels(labels, batch_size = 8)

outputs = model.batch_predict_with_embeds([text], entity_embeddings, labels)

📚 Documentation

Below you can see the table with benchmarking results on various named entity recognition datasets:

📄 License

This project is licensed under the Apache - 2.0 license.

💡 Usage Tip

Connect with our community on Discord for news, support, and discussion about our models. Join Discord.