GLiNER2
Safetensors
Token Classification
Zero-Shot Classification
Text Classification
relation extraction
Structured extraction
Instructions to use bhaskars113/113-gliner-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use bhaskars113/113-gliner-multi with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("bhaskars113/113-gliner-multi") # 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:
- 09276c12afb6b64ef0fe553eb9d092effe9fcdf0292a746be693a157b225dd4f
- Size of remote file:
- 4.31 MB
- SHA256:
- 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3
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