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") entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") print(entities) - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -124,7 +124,7 @@ This repository provides the **best-performing checkpoint** selected from 5 runs
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| Recall | 49.18 |
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| F1 | 52.08 |
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> This checkpoint corresponds to the **seed with the highest strict F1 on the gold evaluation set**.
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| Seed | Precision | Recall | Strict F1 |
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**Summary:**
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- **F1:** mean =
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- **Precision:** mean =
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- **Recall:** mean =
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**Model Selection Strategy:**
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The uploaded checkpoint is the **single best seed** (highest strict F1 on the gold dataset), ensuring strong real-world performance and high-fidelity alignment with domain-expert consensus.
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| Recall | 49.18 |
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| F1 | 52.08 |
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> This checkpoint corresponds to the **seed with the highest strict F1 on the gold evaluation set** (Seed 4 - 33).
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| Seed | Precision | Recall | Strict F1 |
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| 1 | 55.39 | 48.69 | 51.83 |
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| 2 | 58.32 | 44.12 | 50.23 |
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| 3 | 54.80 | 45.92 | 49.97 |
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| 4 | 55.33 | 49.18 | 52.08 |
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| 5 | 51.19 | 43.95 | 47.30 |
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**Summary:**
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- **F1:** mean = 50.28, std = 1.91
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- **Precision:** mean = 55.01, std = 2.54
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- **Recall:** mean = 46.37, std = 2.47
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**Model Selection Strategy:**
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The uploaded checkpoint is the **single best seed** (highest strict F1 on the gold dataset), ensuring strong real-world performance and high-fidelity alignment with domain-expert consensus.
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