Token Classification
SpanMarker
Safetensors
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use lambdavi/span-marker-luke-base-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use lambdavi/span-marker-luke-base-conll2003 with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003") entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") print(entities) - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lambdavi/span-marker-luke-base-conll2003: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/lambdavi/span-marker-luke-base-conll2003/resolve/main/training_args.bin
- Command line
-
hf download hf://lambdavi/span-marker-luke-base-conll2003/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lambdavi/span-marker-luke-base-conll2003/resolve/main/training_args.bin
4.28 kB
- Xet hash:
- 73571cd34cb3184af3dda24757e4100b0dd774afdb13c10fb8d5c48893f976db
- Size of remote file:
- 4.28 kB
- SHA256:
- 880b557718521dd2e59974eec80a476fd41a555554523e3eca2d853e55057d79
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