Token Classification
SpanMarker
TensorBoard
Safetensors
Hebrew
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use iahlt/span-marker-xlm-roberta-base-nemo-mt-he with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use iahlt/span-marker-xlm-roberta-base-nemo-mt-he with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("iahlt/span-marker-xlm-roberta-base-nemo-mt-he") 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 iahlt/span-marker-xlm-roberta-base-nemo-mt-he: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/iahlt/span-marker-xlm-roberta-base-nemo-mt-he/resolve/main/training_args.bin
- Command line
-
hf download hf://iahlt/span-marker-xlm-roberta-base-nemo-mt-he/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/iahlt/span-marker-xlm-roberta-base-nemo-mt-he/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 6cd09017104e5a0569ab4f4117b0253269e26589ddfaaa48fbd161d20ba96ea7
- Size of remote file:
- 4.6 kB
- SHA256:
- d9a9d21735f4f9034cb10f472f4d832aeeb1ba3275fdf590b939e971223d4fbd
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