Token Classification
GLiNER
PyTorch
multilingual
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Model Card for GLiNER-multi

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.

This version has been trained on the Pile-NER dataset (Research purpose). Commercially permission versions are available (urchade/gliner_smallv2, urchade/gliner_mediumv2, urchade/gliner_largev2)

Links

Available models

Release Model Name # of Parameters Language License
v0 urchade/gliner_base
urchade/gliner_multi
209M
209M
English
Multilingual
cc-by-nc-4.0
v1 urchade/gliner_small-v1
urchade/gliner_medium-v1
urchade/gliner_large-v1
166M
209M
459M
English
English
English
cc-by-nc-4.0
v2 urchade/gliner_small-v2
urchade/gliner_medium-v2
urchade/gliner_large-v2
166M
209M
459M
English
English
English
apache-2.0
v2.1 urchade/gliner_small-v2.1
urchade/gliner_medium-v2.1
urchade/gliner_large-v2.1
urchade/gliner_multi-v2.1
166M
209M
459M
209M
English
English
English
Multilingual
apache-2.0

Installation

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

!pip install gliner

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_multi")

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"])
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Saudi Pro League => competitions
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
from gliner import GLiNER

model = GLiNER.from_pretrained("urchade/gliner_multi")

text = """
Это старый-добрый Римантадин, только в сиропе.
"""
# Gold: Римантадин - Drugname, сиропе - Drugform

labels = ["Drugname", "Drugform"]

entities = model.predict_entities(text, labels)

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

Named Entity Recognition benchmark result

image/png

Model Authors

The model authors are:

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}
}
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Dataset used to train urchade/gliner_multi

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