Token Classification
SpanMarker
PyTorch
TensorBoard
Safetensors
multilingual
ner
named-entity-recognition
Eval Results (legacy)
Instructions to use tomaarsen/span-marker-xlm-roberta-base-multinerd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use tomaarsen/span-marker-xlm-roberta-base-multinerd with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-base-multinerd") 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 tomaarsen/span-marker-xlm-roberta-base-multinerd: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/tomaarsen/span-marker-xlm-roberta-base-multinerd/resolve/main/tokenizer.json
- Command line
-
hf download hf://tomaarsen/span-marker-xlm-roberta-base-multinerd/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tomaarsen/span-marker-xlm-roberta-base-multinerd/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- e87f7d8827fe95cb28ae2610465c9e639ec19699d66176f05f464768b462b9ad
- Size of remote file:
- 17.1 MB
- SHA256:
- 7becf84dcb8c923f94254aff98d6e2f21190c901a95f5f4ec1059aa70a1215d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.