--- license: cc-by-nc-4.0 language: - multilingual pipeline_tag: token-classification datasets: - Universal-NER/Pile-NER-type library_name: gliner --- # 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 * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER ## Available models | Release | Model Name | # of Parameters | Language | License | | - | - | - | - | - | | v0 | [urchade/gliner_base](https://huggingface.co/urchade/gliner_base)
[urchade/gliner_multi](https://huggingface.co/urchade/gliner_multi) | 209M
209M | English
Multilingual | cc-by-nc-4.0 | | v1 | [urchade/gliner_small-v1](https://huggingface.co/urchade/gliner_small-v1)
[urchade/gliner_medium-v1](https://huggingface.co/urchade/gliner_medium-v1)
[urchade/gliner_large-v1](https://huggingface.co/urchade/gliner_large-v1) | 166M
209M
459M | English
English
English | cc-by-nc-4.0 | | v2 | [urchade/gliner_small-v2](https://huggingface.co/urchade/gliner_small-v2)
[urchade/gliner_medium-v2](https://huggingface.co/urchade/gliner_medium-v2)
[urchade/gliner_large-v2](https://huggingface.co/urchade/gliner_large-v2) | 166M
209M
459M | English
English
English | apache-2.0 | | v2.1 | [urchade/gliner_small-v2.1](https://huggingface.co/urchade/gliner_small-v2.1)
[urchade/gliner_medium-v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)
[urchade/gliner_large-v2.1](https://huggingface.co/urchade/gliner_large-v2.1)
[urchade/gliner_multi-v2.1](https://huggingface.co/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`. ```python 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 ``` ```python 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](https://cdn-uploads.huggingface.co/production/uploads/6317233cc92fd6fee317e030/Y5f7tK8lonGqeeO6L6bVI.png) ## Model Authors The model authors are: * [Urchade Zaratiana](https://huggingface.co/urchade) * 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} } ```