Token Classification
SpanMarker
PyTorch
Safetensors
German
ner
named-entity-recognition
Eval Results (legacy)
Instructions to use gwlms/span-marker-token-dropping-bert-germeval14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use gwlms/span-marker-token-dropping-bert-germeval14 with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("gwlms/span-marker-token-dropping-bert-germeval14") - Notebooks
- Google Colab
- Kaggle
readme: fix table
Browse files
README.md
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@@ -59,9 +59,9 @@ Evaluation is performed with SpanMarkers internal evaluation code that uses `seq
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We fine-tune 5 models and upload the model with best F1-Score on development set. Results on development set are
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| Model
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| GWLMS Token Dropping BERT
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The best model achieves a final test score of 87.44%.
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We fine-tune 5 models and upload the model with best F1-Score on development set. Results on development set are
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in brackets:
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| Model | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg.
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| GWLMS Token Dropping BERT | (87.85) / 87.28 | (**88.09**) / 87.44 | (87.59) / 87.26 | (87.71) / 87.43 | (87.83) / 87.24 | (87.81) / 87.33
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The best model achieves a final test score of 87.44%.
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