Token Classification
SpanMarker
Safetensors
English
ner
named-entity-recognition
generated_from_span_marker_trainer
climate-change
earth-science
Eval Results (legacy)
Instructions to use P0L3/CliReNER-sciclimatebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use P0L3/CliReNER-sciclimatebert with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("P0L3/CliReNER-sciclimatebert") 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 P0L3/CliReNER-sciclimatebert: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/P0L3/CliReNER-sciclimatebert/resolve/main/training_args.bin
- Command line
-
hf download hf://P0L3/CliReNER-sciclimatebert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/P0L3/CliReNER-sciclimatebert/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 498d205af097c6dc1366f3b97331f02acfa6645cb7c4282e78a75559c290749a
- Size of remote file:
- 5.91 kB
- SHA256:
- 5262e5a6c466567547790fdd2f1a4fe5437fe87cfe9eb74a863c8734b053ba69
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