Token Classification
SpanMarker
Safetensors
ner
named-entity-recognition
mdeberta-v3
multinerd
multilingual
information-extraction
Eval Results (legacy)
Instructions to use Rishabh157/spanmarker-multinerd-mdeberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use Rishabh157/spanmarker-multinerd-mdeberta with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("Rishabh157/spanmarker-multinerd-mdeberta") entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") print(entities) - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Rishabh157/spanmarker-multinerd-mdeberta: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/Rishabh157/spanmarker-multinerd-mdeberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://Rishabh157/spanmarker-multinerd-mdeberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Rishabh157/spanmarker-multinerd-mdeberta/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- 5597865faeb86269f652fd1abe3d2da300019ca0e5ae8653ec87f353faef28ca
- Size of remote file:
- 16.4 MB
- SHA256:
- f7131bd48bd01b64d0d3e5fbd77ccbc03252db583b4b94ad3455db76f150fbd1
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