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") - GLiNER2
How to use ottema/gliner2-ptbr with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.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
- Xet hash:
- ba75cafa615417b5fc120088385494f01665c2c4de9b297e7ef6857700dbc0ba
- Size of remote file:
- 16.3 MB
- SHA256:
- a1c7ccb287623cccb7c03150953b6d2a09dd95122933393c9151c3a60095c97e
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