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-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use P0L3/CliReNER-roberta-base with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("P0L3/CliReNER-roberta-base") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -186,14 +186,21 @@ pip install span_marker
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from span_marker import SpanMarkerModel
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# Download from the 🤗 Hub
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model = SpanMarkerModel.from_pretrained("
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# Run inference
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text = "
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entities = model.predict(text)
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for entity in entities:
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print(f"Entity: {entity['span']} | Label: {entity['label']} | Score: {entity['score']:.4f}")
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```
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### Downstream Use
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from span_marker import SpanMarkerModel
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# Download from the 🤗 Hub
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model = SpanMarkerModel.from_pretrained("P0L3/CliReNER-roberta-base")
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# Run inference
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text = "Anthropogenic climate change is fundamentally altering weather patterns and climate extremes, causing widespread adverse impacts to both nature and human systems (IPCC 2023)."
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entities = model.predict(text)
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for entity in entities:
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print(f"Entity: {entity['span']} | Label: {entity['label']} | Score: {entity['score']:.4f}")
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# Entity: climate change | Label: Meteorological Phenomenon | Score: 0.4065
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# Entity: weather patterns | Label: Meteorological Phenomenon | Score: 0.6808
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# Entity: climate extremes | Label: Meteorological Phenomenon | Score: 0.7115
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# Entity: nature | Label: Other | Score: 0.4608
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# Entity: human systems | Label: System | Score: 0.6562
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# Entity: IPCC 2023 | Label: Other | Score: 0.4812
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```
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### Downstream Use
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