Token Classification
GLiNER2
Safetensors
English
extractor
information-extraction
named-entity-recognition
relation-extraction
event-extraction
text-classification
Instructions to use whr778/gliner2-large-v1-wikievents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use whr778/gliner2-large-v1-wikievents with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("whr778/gliner2-large-v1-wikievents") # 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:
- 08427f12b5e1ce8e8ca551eed1892082b90a6c4b4a52ea59255e00aace896257
- Size of remote file:
- 1.95 GB
- SHA256:
- 4fade1a26cdfebf7f41a5da78c77eb75e6a3f8c657b28d916b56113d572b5518
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