Token Classification
SpanMarker
PyTorch
English
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use PooRaj/span-marker-bert-base-fewnerd-coarse-super with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use PooRaj/span-marker-bert-base-fewnerd-coarse-super with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("PooRaj/span-marker-bert-base-fewnerd-coarse-super") 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 PooRaj/span-marker-bert-base-fewnerd-coarse-super: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/PooRaj/span-marker-bert-base-fewnerd-coarse-super/resolve/main/training_args.bin
- Command line
-
hf download hf://PooRaj/span-marker-bert-base-fewnerd-coarse-super/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PooRaj/span-marker-bert-base-fewnerd-coarse-super/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- f53ee72d21f4c3548404783a0443d4c1af6809c95f5be6984f67d241ef231939
- Size of remote file:
- 4.09 kB
- SHA256:
- 0fcb48bf923c03c391e1aa088f9ff653f6951e0911d6aaf1bc94541fdb29c1e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.