Token Classification
GLiNER
PyTorch
English
ner
named-entity-recognition
climate-change
earth-science
biology
Instructions to use P0L3/CliReNER-gliner_medium-v2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use P0L3/CliReNER-gliner_medium-v2.5 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("P0L3/CliReNER-gliner_medium-v2.5") 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 P0L3/CliReNER-gliner_medium-v2.5: direct link, hf CLI and curl.
- Browser
- Download file 834 MB
-
https://huggingface.co/P0L3/CliReNER-gliner_medium-v2.5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://P0L3/CliReNER-gliner_medium-v2.5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/P0L3/CliReNER-gliner_medium-v2.5/resolve/main/pytorch_model.bin
834 MB
- Xet hash:
- a9ed8d6b40955f03a8135b5c8d20c68e4d7bd42fba5aa07ffe552bf0d11325e6
- Size of remote file:
- 834 MB
- SHA256:
- 0ba78e7ec3886653311a88048f27ea573582814881cbe65497ead980a23a75f7
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