Token Classification
GLiNER
PyTorch
Catalan
Spanish
NER
Catalan
NLP
television transcriptions
manual annotation
GLiNER
Instructions to use Ugiat/NERCat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use Ugiat/NERCat with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("Ugiat/NERCat") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -99,7 +99,7 @@ The dataset included 13,732 named entity instances across eight categories:
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```
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@misc{article_id,
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title = {NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan},
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author = {Guillem Cadevall Ferreres, Marc Bardeli Gámez, Marc Serrano Sanz, Pol Gerdt
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year = {2025},
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archivePrefix = {arXiv},
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url = {https://github.com/ugiat/NERCat/blob/main/Catalan_GLiNER_Paper.pdf}
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```
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@misc{article_id,
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title = {NERCat: Fine-Tuning for Enhanced Named Entity Recognition in Catalan},
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author = {Guillem Cadevall Ferreres, Marc Bardeli Gámez, Marc Serrano Sanz, Pol Gerdt Basullas, Francesc Tarres Ruiz, Raul Quijada Ferrero},
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year = {2025},
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archivePrefix = {arXiv},
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url = {https://github.com/ugiat/NERCat/blob/main/Catalan_GLiNER_Paper.pdf}
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