Instructions to use VAGOsolutions/SauerkrautLM-GLiNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use VAGOsolutions/SauerkrautLM-GLiNER with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("VAGOsolutions/SauerkrautLM-GLiNER") 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 VAGOsolutions/SauerkrautLM-GLiNER: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/VAGOsolutions/SauerkrautLM-GLiNER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VAGOsolutions/SauerkrautLM-GLiNER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VAGOsolutions/SauerkrautLM-GLiNER/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- 6476cc7f80b97d1ebb1b5a957ee8208abaa0264483da0ded180202a44bacf4d7
- Size of remote file:
- 1.33 GB
- SHA256:
- a4f25b51796426ca39bf5b59ea2d142902693b690ffc1458a792267e45e3c269
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