Token Classification
GLiNER
PyTorch
multilingual
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About

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.

Links

Installation

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

!pip install gliner -U

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("gliner-community/gliner_small-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"])
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

Named Entity Recognition benchmark result

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

Results on other datasets

Model Dataset Precision Recall F1 Score
gliner-community/gliner_small-v2.5 ACE 2004 35.18% 22.81% 27.67%
ACE 2005 35.89% 22.39% 27.58%
AnatEM 49.12% 31.31% 38.24%
Broad Tweet Corpus 59.51% 77.85% 67.46%
CoNLL 2003 63.16% 70.43% 66.60%
FabNER 23.78% 22.55% 23.15%
FindVehicle 37.46% 40.06% 38.72%
GENIA_NER 45.90% 54.11% 49.67%
HarveyNER 13.20% 32.58% 18.78%
MultiNERD 45.87% 87.01% 60.07%
Ontonotes 23.05% 41.16% 29.55%
PolyglotNER 31.88% 67.22% 43.25%
TweetNER7 40.98% 39.91% 40.44%
WikiANN en 55.35% 60.06% 57.61%
WikiNeural 64.52% 86.24% 73.81%
bc2gm 51.70% 49.99% 50.83%
bc4chemd 30.78% 57.56% 40.11%
bc5cdr 63.48% 69.65% 66.42%
ncbi 63.36% 66.67% 64.97%
Average 46.58%
------------------------------------ --------------------- ----------- -------- ----------
urchade/gliner_small-v2.1 ACE 2004 38.89% 23.53% 29.32%
ACE 2005 42.09% 26.82% 32.76%
AnatEM 63.71% 19.45% 29.80%
Broad Tweet Corpus 57.01% 70.49% 63.04%
CoNLL 2003 57.11% 62.66% 59.76%
FabNER 32.41% 12.33% 17.87%
FindVehicle 43.47% 33.02% 37.53%
GENIA_NER 61.03% 37.25% 46.26%
HarveyNER 23.12% 15.16% 18.32%
MultiNERD 43.63% 83.60% 57.34%
Ontonotes 23.25% 35.41% 28.07%
PolyglotNER 29.47% 64.41% 40.44%
TweetNER7 44.78% 30.83% 36.52%
WikiANN en 52.58% 58.31% 55.30%
WikiNeural 53.38% 82.19% 64.72%
bc2gm 66.64% 30.56% 41.90%
bc4chemd 42.01% 56.03% 48.02%
bc5cdr 72.03% 58.58% 64.61%
ncbi 68.88% 46.71% 55.67%
Average 43.54%
------------------------------------ --------------------- ----------- -------- ----------
EmergentMethods/gliner_small-v2.1 ACE 2004 39.92% 17.50% 24.34%
ACE 2005 38.53% 16.58% 23.18%
AnatEM 55.95% 25.69% 35.22%
Broad Tweet Corpus 66.63% 72.00% 69.21%
CoNLL 2003 62.89% 58.96% 60.86%
FabNER 32.76% 13.33% 18.95%
FindVehicle 42.93% 43.20% 43.06%
GENIA_NER 51.28% 43.75% 47.22%
HarveyNER 24.82% 21.52% 23.05%
MultiNERD 59.27% 80.69% 68.34%
Ontonotes 32.97% 37.59% 35.13%
PolyglotNER 33.60% 63.30% 43.90%
TweetNER7 46.90% 28.66% 35.58%
WikiANN en 51.91% 55.43% 53.61%
WikiNeural 70.65% 82.21% 75.99%
bc2gm 49.95% 43.13% 46.29%
bc4chemd 35.88% 71.64% 47.81%
bc5cdr 68.41% 68.90% 68.65%
ncbi 55.31% 59.87% 57.50%
Average 46.20%
----------------------------------------- --------------------- ----------- -------- ----------
gliner-community/gliner_medium-v2.5 ACE 2004 33.06% 20.96% 25.66%
ACE 2005 33.65% 19.65% 24.81%
AnatEM 52.03% 35.28% 42.05%
Broad Tweet Corpus 60.57% 79.09% 68.60%
CoNLL 2003 63.80% 68.31% 65.98%
FabNER 26.20% 22.26% 24.07%
FindVehicle 41.95% 40.68% 41.30%
GENIA_NER 51.83% 62.34% 56.60%
HarveyNER 14.04% 32.17% 19.55%
MultiNERD 47.63% 88.78% 62.00%
Ontonotes 21.68% 38.41% 27.71%
PolyglotNER 32.73% 68.27% 44.24%
TweetNER7 40.39% 37.64% 38.97%
WikiANN en 56.41% 59.90% 58.10%
WikiNeural 65.61% 86.28% 74.54%
bc2gm 55.20% 56.71% 55.95%
bc4chemd 35.94% 63.67% 45.94%
bc5cdr 63.50% 70.09% 66.63%
ncbi 62.96% 68.55% 65.63%
Average 47.81%
----------------------------------------- --------------------- ----------- -------- ----------
urchade/gliner_medium-v2.1 ACE 2004 36.33% 22.74% 27.97%
ACE 2005 40.49% 25.46% 31.27%
AnatEM 59.75% 16.87% 26.31%
Broad Tweet Corpus 60.89% 67.25% 63.91%
CoNLL 2003 60.62% 62.39% 61.50%
FabNER 27.72% 12.24% 16.98%
FindVehicle 41.55% 31.31% 35.71%
GENIA_NER 60.86% 43.93% 51.03%
HarveyNER 23.20% 23.16% 23.18%
MultiNERD 41.25% 83.74% 55.27%
Ontonotes 20.58% 34.11% 25.67%
PolyglotNER 31.32% 64.22% 42.11%
TweetNER7 44.52% 33.42% 38.18%
WikiANN en 54.57% 56.47% 55.51%
WikiNeural 57.60% 81.57% 67.52%
bc2gm 67.98% 33.45% 44.84%
bc4chemd 45.66% 52.00% 48.62%
bc5cdr 72.20% 58.12% 64.40%
ncbi 73.12% 49.74% 59.20%
Average 44.17%
----------------------------------------- --------------------- ----------- -------- ----------
EmergentMethods/gliner_news_medium-v2.1 ACE 2004 39.21% 17.24% 23.95%
ACE 2005 39.82% 16.48% 23.31%
AnatEM 57.67% 23.57% 33.46%
Broad Tweet Corpus 69.52% 65.94% 67.69%
CoNLL 2003 68.26% 58.45% 62.97%
FabNER 30.74% 15.51% 20.62%
FindVehicle 40.33% 37.37% 38.79%
GENIA_NER 53.70% 47.73% 50.54%
HarveyNER 26.29% 27.05% 26.67%
MultiNERD 56.78% 81.96% 67.08%
Ontonotes 30.90% 35.86% 33.19%
PolyglotNER 35.98% 60.96% 45.25%
TweetNER7 52.37% 30.50% 38.55%
WikiANN en 53.81% 52.29% 53.04%
WikiNeural 76.84% 78.92% 77.86%
bc2gm 62.97% 44.24% 51.96%
bc4chemd 44.90% 65.56% 53.30%
bc5cdr 73.93% 67.03% 70.31%
ncbi 69.53% 60.82% 64.88%
Average 47.55%
----------------------------------------- --------------------- ----------- -------- ----------
gliner-community/gliner_large-v2.5 ACE 2004 31.64% 22.81% 26.51%
ACE 2005 32.10% 22.56% 26.49%
AnatEM 53.64% 27.82% 36.64%
Broad Tweet Corpus 61.93% 76.85% 68.59%
CoNLL 2003 62.83% 67.71% 65.18%
FabNER 24.54% 27.03% 25.73%
FindVehicle 40.71% 56.24% 47.23%
GENIA_NER 43.56% 52.56% 47.64%
HarveyNER 14.85% 27.05% 19.17%
MultiNERD 38.04% 89.17% 53.33%
Ontonotes 17.28% 40.16% 24.16%
PolyglotNER 32.88% 63.31% 43.28%
TweetNER7 38.03% 41.43% 39.66%
WikiANN en 57.80% 60.54% 59.14%
WikiNeural 67.72% 83.94% 74.96%
bc2gm 54.74% 48.54% 51.45%
bc4chemd 40.20% 58.66% 47.71%
bc5cdr 66.27% 71.95% 69.00%
ncbi 68.09% 61.55% 64.65%
Average 46.87%
----------------------------------------- --------------------- ----------- -------- ----------
urchade/gliner_large-v2.1 ACE 2004 37.52% 25.38% 30.28%
ACE 2005 39.02% 29.00% 33.27%
AnatEM 52.86% 13.64% 21.68%
Broad Tweet Corpus 51.44% 71.73% 59.91%
CoNLL 2003 54.86% 64.98% 59.49%
FabNER 23.98% 16.00% 19.19%
FindVehicle 47.04% 57.53% 51.76%
GENIA_NER 58.10% 49.98% 53.74%
HarveyNER 16.29% 21.93% 18.69%
MultiNERD 34.09% 85.43% 48.74%
Ontonotes 14.02% 32.01% 19.50%
PolyglotNER 28.53% 64.92% 39.64%
TweetNER7 38.00% 34.34% 36.08%
WikiANN en 51.69% 59.92% 55.50%
WikiNeural 50.94% 82.08% 62.87%
bc2gm 64.48% 32.47% 43.19%
bc4chemd 48.66% 57.52% 52.72%
bc5cdr 72.19% 64.27% 68.00%
ncbi 69.54% 52.25% 59.67%
Average 43.89%
----------------------------------------- --------------------- ----------- -------- ----------
EmergenMethods/fliner_news_large-v2.1 ACE 2004 43.19% 18.39% 25.80%
ACE 2005 45.24% 21.20% 28.87%
AnatEM 61.51% 21.66% 32.04%
Broad Tweet Corpus 69.38% 68.99% 69.18%
CoNLL 2003 61.47% 52.18% 56.45%
FabNER 27.42% 19.11% 22.52%
FindVehicle 46.30% 62.48% 53.19%
GENIA_NER 54.13% 54.02% 54.07%
HarveyNER 15.91% 15.78% 15.84%
MultiNERD 53.73% 79.07% 63.98%
Ontonotes 26.78% 39.77% 32.01%
PolyglotNER 34.28% 55.87% 42.49%
TweetNER7 48.06% 28.18% 35.53%
WikiANN en 53.66% 51.34% 52.47%
WikiNeural 69.81% 70.75% 70.28%
bc2gm 59.83% 37.62% 46.20%
bc4chemd 46.24% 69.15% 55.42%
bc5cdr 71.94% 70.37% 71.15%
ncbi 70.17% 61.44% 65.52%
Average 47.00%
----------------------------------------- --------------------- ----------- -------- ----------

Other 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

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 gliner-community/gliner_small-v2.5