Token Classification
SpanMarker
Safetensors
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use lambdavi/span-marker-luke-base-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use lambdavi/span-marker-luke-base-conll2003 with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003") 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 lambdavi/span-marker-luke-base-conll2003: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/lambdavi/span-marker-luke-base-conll2003/resolve/main/tokenizer.json
- Command line
-
hf download hf://lambdavi/span-marker-luke-base-conll2003/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/lambdavi/span-marker-luke-base-conll2003/resolve/main/tokenizer.json
2.11 MB
File too large to display, you can check the raw version instead.