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sassoftware/
glitext-small

urchade/pile-mistral-v0.1

2311.08526

apache-2.0

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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.

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.

python

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:

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:

Urchade Zaratiana
Ihor Stepanov
Nadi Tomeh
Pierre Holat
Thierry Charnois

Citation

bibtex

@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}
}

Source Model Repo

This model is derived from gliner-community/gliner_small-v2.5. See the upstream repository for the original safetensors weights, training data, and the full upstream model card.

ONNX Weights

ONNX weights added by SAS — converted from the upstream safetensors checkpoint.

File in this repo: model.onnx.

Using this Model with the SAS GLiText API

This repo is consumed by the SAS GLiText product. To download it onto a SAS GLiText server:

POST /v1/models/download?name=small

To download and load into memory in one step:

PUT /v1/models?name=small

Security Scan

Scanned with modelaudit v0.2.40 on 2026-04-27. 46/46 checks passed. Full results.

File	Size	SHA-256
`model.onnx`	664.8 MB	`9cc27f025c07c318…`
`model_fp16.onnx`	333.0 MB	`9e14220fb41ebe6c…`
`model_int8.onnx`	196.8 MB	`791f20d6f34d4a4d…`

sassoftware/glitext-small