Token Classification
SpanMarker
Safetensors
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results (legacy)
Instructions to use lambdavi/span-marker-luke-base-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SpanMarker
How to use lambdavi/span-marker-luke-base-conll2003 with SpanMarker:
from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003") entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") print(entities) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -55,6 +55,12 @@ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained
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## Model Details
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### Model Description
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- **Model Type:** SpanMarker
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<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
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## Model Details
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Important Note: I used the Tokenizer from "roberta-base".
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```
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tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.tokenizer.config)
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model.set_tokenizer(tokenizer)
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```
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### Model Description
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- **Model Type:** SpanMarker
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<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
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