Token Classification
GLiNER
English
named-entity-recognition
data-mention-extraction
survey-detection
geocoded-data
DHS
census
ml-intern
Instructions to use rafmacalaba/gliner-large-data-mentions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use rafmacalaba/gliner-large-data-mentions with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rafmacalaba/gliner-large-data-mentions") 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
Commit History
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Update ML Intern artifact metadata
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Add comprehensive README with entity schema, training config, and agent execution plan
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Add complete GLiNER fine-tuning script for data mention extraction
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Update ML Intern artifact metadata
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initial commit
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