Token Classification
SpanMarker
PyTorch
English
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use jackboyla/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 jackboyla/span-marker-bert-base-fewnerd-coarse-super with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("jackboyla/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 pytorch_model.bin from jackboyla/span-marker-bert-base-fewnerd-coarse-super: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/jackboyla/span-marker-bert-base-fewnerd-coarse-super/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jackboyla/span-marker-bert-base-fewnerd-coarse-super/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jackboyla/span-marker-bert-base-fewnerd-coarse-super/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- f3eded5414545e2da293c4e6d063c303fd9b2f86a5fc951837ec50615ecdfbeb
- Size of remote file:
- 433 MB
- SHA256:
- 4355ee012cf32aa1dedfa8dfdfecce817dd75c6efd2c852aad3653c53fc390ea
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