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 pytorch_model.bin from PooRaj/span-marker-bert-base-fewnerd-coarse-super: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/PooRaj/span-marker-bert-base-fewnerd-coarse-super/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PooRaj/span-marker-bert-base-fewnerd-coarse-super/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PooRaj/span-marker-bert-base-fewnerd-coarse-super/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 58db6170f87e31ba255137cea3a86d258d4276db2d807690a190367f06522913
- Size of remote file:
- 433 MB
- SHA256:
- cd691a5f017ffdee143800b05c85a355f805478ccebf15cfb0f015ab18b0b40a
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