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-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use P0L3/CliReNER-bert-base-uncased with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("P0L3/CliReNER-bert-base-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f96bd0e0749f2dde25eaa54c2b1459d5f0871be6005b3eafb239de1940c260ad
- Size of remote file:
- 5.91 kB
- SHA256:
- 849979c870cc496fedefb3e47f376f43d9da5d417dabdacb8f17abf363f2fbef
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