Instructions to use rafmacalaba/gliner_datause with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use rafmacalaba/gliner_datause with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("rafmacalaba/gliner_datause") 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
Download pytorch_model.bin from rafmacalaba/gliner_datause: direct link, hf CLI and curl.
- Browser
- Download file 1.78 GB
-
https://huggingface.co/rafmacalaba/gliner_datause/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rafmacalaba/gliner_datause/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rafmacalaba/gliner_datause/resolve/main/pytorch_model.bin
1.78 GB
- Xet hash:
- 9376e6c6936b02dd57f49697eb4e5c68ffc3794cf6bac2e1fedb42eaab7ab965
- Size of remote file:
- 1.78 GB
- SHA256:
- 8b4ec58e817f0648ce1f89c12e33d866c4a580d56e09d901184dc704ef631468
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