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Характеристики

Размер весов
1,3 ГБ
Лицензия
Apache 2.0
Библиотека
gliner
Опубликована
17 июн 2024

gliner_small-v2.5

Разметка токеновТекст → label9 профессий

Мультиязычная модель GLiNER извлекает из текста сущности заданных пользователем типов для задач распознавания именованных сущностей.

2 853скачиваний за 30 дней14лайковвеса обновлены28 апр 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

gliner-community/gliner_small-v2.5 — мультиязычная модель распознавания именованных сущностей GLiNER от сообщества GLiNER. Она использует двунаправленный трансформерный энкодер BERT-типа и позволяет задавать типы сущностей для извлечения, поэтому подходит для сценариев, где заранее заданный фиксированный набор меток неудобен. Модель работает с текстом через библиотеку GLiNER и возвращает найденные сущности с их типами и позициями.

Модель предназначена для извлечения имен, организаций, мест и других заданных пользователем сущностей из документов и текстовых потоков. В README приведены результаты оценки на нескольких наборах данных NER, включая ACE 2004, ACE 2005 и AnatEM. Репозиторий содержит веса PyTorch и safetensors; лицензия Apache-2.0 разрешает использование в соответствии с её условиями. По сравнению с более крупными LLM, GLiNER ориентирована на практичное распознавание сущностей при ограниченных вычислительных ресурсах.

Сценарии применения

  • извлечение имён людей и организаций из документов
  • поиск пользовательских типов сущностей в тексте
  • разметка сущностей в многоязычных текстовых корпусах

Ограничения

Качество зависит от текста и заданных типов сущностей; приведённые в README оценки на отдельных наборах данных показывают, что полнота распознавания может быть ограниченной. Метаданные и карточка не уточняют перечень поддерживаемых языков для этой версии.

Для каких профессий подходит9

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Также подходит для

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

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

ModelDatasetPrecisionRecallF1 Score
gliner-community/gliner_small-v2.5ACE 200435.18%22.81%27.67%
ACE 200535.89%22.39%27.58%
AnatEM49.12%31.31%38.24%
Broad Tweet Corpus59.51%77.85%67.46%
CoNLL 200363.16%70.43%66.60%
FabNER23.78%22.55%23.15%
FindVehicle37.46%40.06%38.72%
GENIA_NER45.90%54.11%49.67%
HarveyNER13.20%32.58%18.78%
MultiNERD45.87%87.01%60.07%
Ontonotes23.05%41.16%29.55%
PolyglotNER31.88%67.22%43.25%
TweetNER740.98%39.91%40.44%
WikiANN en55.35%60.06%57.61%
WikiNeural64.52%86.24%73.81%
bc2gm51.70%49.99%50.83%
bc4chemd30.78%57.56%40.11%
bc5cdr63.48%69.65%66.42%
ncbi63.36%66.67%64.97%
Average46.58%
--------------------------------------------------------------------------------------
urchade/gliner_small-v2.1ACE 200438.89%23.53%29.32%
ACE 200542.09%26.82%32.76%
AnatEM63.71%19.45%29.80%
Broad Tweet Corpus57.01%70.49%63.04%
CoNLL 200357.11%62.66%59.76%
FabNER32.41%12.33%17.87%
FindVehicle43.47%33.02%37.53%
GENIA_NER61.03%37.25%46.26%
HarveyNER23.12%15.16%18.32%
MultiNERD43.63%83.60%57.34%
Ontonotes23.25%35.41%28.07%
PolyglotNER29.47%64.41%40.44%
TweetNER744.78%30.83%36.52%
WikiANN en52.58%58.31%55.30%
WikiNeural53.38%82.19%64.72%
bc2gm66.64%30.56%41.90%
bc4chemd42.01%56.03%48.02%
bc5cdr72.03%58.58%64.61%
ncbi68.88%46.71%55.67%
Average43.54%
--------------------------------------------------------------------------------------
EmergentMethods/gliner_small-v2.1ACE 200439.92%17.50%24.34%
ACE 200538.53%16.58%23.18%
AnatEM55.95%25.69%35.22%
Broad Tweet Corpus66.63%72.00%69.21%
CoNLL 200362.89%58.96%60.86%
FabNER32.76%13.33%18.95%
FindVehicle42.93%43.20%43.06%
GENIA_NER51.28%43.75%47.22%
HarveyNER24.82%21.52%23.05%
MultiNERD59.27%80.69%68.34%
Ontonotes32.97%37.59%35.13%
PolyglotNER33.60%63.30%43.90%
TweetNER746.90%28.66%35.58%
WikiANN en51.91%55.43%53.61%
WikiNeural70.65%82.21%75.99%
bc2gm49.95%43.13%46.29%
bc4chemd35.88%71.64%47.81%
bc5cdr68.41%68.90%68.65%
ncbi55.31%59.87%57.50%
Average46.20%
-------------------------------------------------------------------------------------------
gliner-community/gliner_medium-v2.5ACE 200433.06%20.96%25.66%
ACE 200533.65%19.65%24.81%
AnatEM52.03%35.28%42.05%
Broad Tweet Corpus60.57%79.09%68.60%
CoNLL 200363.80%68.31%65.98%
FabNER26.20%22.26%24.07%
FindVehicle41.95%40.68%41.30%
GENIA_NER51.83%62.34%56.60%
HarveyNER14.04%32.17%19.55%
MultiNERD47.63%88.78%62.00%
Ontonotes21.68%38.41%27.71%
PolyglotNER32.73%68.27%44.24%
TweetNER740.39%37.64%38.97%
WikiANN en56.41%59.90%58.10%
WikiNeural65.61%86.28%74.54%
bc2gm55.20%56.71%55.95%
bc4chemd35.94%63.67%45.94%
bc5cdr63.50%70.09%66.63%
ncbi62.96%68.55%65.63%
Average47.81%
-------------------------------------------------------------------------------------------
urchade/gliner_medium-v2.1ACE 200436.33%22.74%27.97%
ACE 200540.49%25.46%31.27%
AnatEM59.75%16.87%26.31%
Broad Tweet Corpus60.89%67.25%63.91%
CoNLL 200360.62%62.39%61.50%
FabNER27.72%12.24%16.98%
FindVehicle41.55%31.31%35.71%
GENIA_NER60.86%43.93%51.03%
HarveyNER23.20%23.16%23.18%
MultiNERD41.25%83.74%55.27%
Ontonotes20.58%34.11%25.67%
PolyglotNER31.32%64.22%42.11%
TweetNER744.52%33.42%38.18%
WikiANN en54.57%56.47%55.51%
WikiNeural57.60%81.57%67.52%
bc2gm67.98%33.45%44.84%
bc4chemd45.66%52.00%48.62%
bc5cdr72.20%58.12%64.40%
ncbi73.12%49.74%59.20%
Average44.17%
-------------------------------------------------------------------------------------------
EmergentMethods/gliner_news_medium-v2.1ACE 200439.21%17.24%23.95%
ACE 200539.82%16.48%23.31%
AnatEM57.67%23.57%33.46%
Broad Tweet Corpus69.52%65.94%67.69%
CoNLL 200368.26%58.45%62.97%
FabNER30.74%15.51%20.62%
FindVehicle40.33%37.37%38.79%
GENIA_NER53.70%47.73%50.54%
HarveyNER26.29%27.05%26.67%
MultiNERD56.78%81.96%67.08%
Ontonotes30.90%35.86%33.19%
PolyglotNER35.98%60.96%45.25%
TweetNER752.37%30.50%38.55%
WikiANN en53.81%52.29%53.04%
WikiNeural76.84%78.92%77.86%
bc2gm62.97%44.24%51.96%
bc4chemd44.90%65.56%53.30%
bc5cdr73.93%67.03%70.31%
ncbi69.53%60.82%64.88%
Average47.55%
-------------------------------------------------------------------------------------------
gliner-community/gliner_large-v2.5ACE 200431.64%22.81%26.51%
ACE 200532.10%22.56%26.49%
AnatEM53.64%27.82%36.64%
Broad Tweet Corpus61.93%76.85%68.59%
CoNLL 200362.83%67.71%65.18%
FabNER24.54%27.03%25.73%
FindVehicle40.71%56.24%47.23%
GENIA_NER43.56%52.56%47.64%
HarveyNER14.85%27.05%19.17%
MultiNERD38.04%89.17%53.33%
Ontonotes17.28%40.16%24.16%
PolyglotNER32.88%63.31%43.28%
TweetNER738.03%41.43%39.66%
WikiANN en57.80%60.54%59.14%
WikiNeural67.72%83.94%74.96%
bc2gm54.74%48.54%51.45%
bc4chemd40.20%58.66%47.71%
bc5cdr66.27%71.95%69.00%
ncbi68.09%61.55%64.65%
Average46.87%
-------------------------------------------------------------------------------------------
urchade/gliner_large-v2.1ACE 200437.52%25.38%30.28%
ACE 200539.02%29.00%33.27%
AnatEM52.86%13.64%21.68%
Broad Tweet Corpus51.44%71.73%59.91%
CoNLL 200354.86%64.98%59.49%
FabNER23.98%16.00%19.19%
FindVehicle47.04%57.53%51.76%
GENIA_NER58.10%49.98%53.74%
HarveyNER16.29%21.93%18.69%
MultiNERD34.09%85.43%48.74%
Ontonotes14.02%32.01%19.50%
PolyglotNER28.53%64.92%39.64%
TweetNER738.00%34.34%36.08%
WikiANN en51.69%59.92%55.50%
WikiNeural50.94%82.08%62.87%
bc2gm64.48%32.47%43.19%
bc4chemd48.66%57.52%52.72%
bc5cdr72.19%64.27%68.00%
ncbi69.54%52.25%59.67%
Average43.89%
-------------------------------------------------------------------------------------------
EmergenMethods/fliner_news_large-v2.1ACE 200443.19%18.39%25.80%
ACE 200545.24%21.20%28.87%
AnatEM61.51%21.66%32.04%
Broad Tweet Corpus69.38%68.99%69.18%
CoNLL 200361.47%52.18%56.45%
FabNER27.42%19.11%22.52%
FindVehicle46.30%62.48%53.19%
GENIA_NER54.13%54.02%54.07%
HarveyNER15.91%15.78%15.84%
MultiNERD53.73%79.07%63.98%
Ontonotes26.78%39.77%32.01%
PolyglotNER34.28%55.87%42.49%
TweetNER748.06%28.18%35.53%
WikiANN en53.66%51.34%52.47%
WikiNeural69.81%70.75%70.28%
bc2gm59.83%37.62%46.20%
bc4chemd46.24%69.15%55.42%
bc5cdr71.94%70.37%71.15%
ncbi70.17%61.44%65.52%
Average47.00%
-------------------------------------------------------------------------------------------

Other available models

ReleaseModel Name# of ParametersLanguageLicense
v0urchade/gliner_baseurchade/gliner_multi209M209MEnglishMultilingualcc-by-nc-4.0
v1urchade/gliner_small-v1urchade/gliner_medium-v1urchade/gliner_large-v1166M209M459MEnglish English Englishcc-by-nc-4.0
v2urchade/gliner_small-v2urchade/gliner_medium-v2urchade/gliner_large-v2166M209M459MEnglish English Englishapache-2.0
v2.1urchade/gliner_small-v2.1urchade/gliner_medium-v2.1urchade/gliner_large-v2.1 urchade/gliner_multi-v2.1166M209M459M209MEnglish English English Multilingualapache-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}
}

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · gliner

Нельзя запустить

Модели GLiNER требуют отдельного рантайма.

Ревизия весов
f227d3c

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