Token Classification
GLiNER
Safetensors
GLiNER2
Portuguese
extractor
portuguese
pt-br
brazilian-portuguese
ner
named-entity-recognition
open-vocabulary-ner
information-extraction
schema-guided-extraction
ontology-guided-extraction
operational-evidence
service-triage
technical-support
education
assistance
ottema
Instructions to use ottema/gliner2-ptbr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use ottema/gliner2-ptbr with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("ottema/gliner2-ptbr") 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"]) - GLiNER2
How to use ottema/gliner2-ptbr with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("ottema/gliner2-ptbr") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
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