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Update README.md
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README.md
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@@ -37,6 +37,22 @@ The model will predict the following four entities:
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| `MISC` | Miscellaneous | A named entity of a different kind (e.g., *British Pound* or *Mona Lisa*) |
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## Performance
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The following is the Micro-F1 NER performance on Scandinavian NER test datasets, compared with the current state-of-the-art. The models have been evaluated on the test set along with 9 bootstrapped versions of it, with the mean and 95% confidence interval shown here:
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| `MISC` | Miscellaneous | A named entity of a different kind (e.g., *British Pound* or *Mona Lisa*) |
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## Use
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You can use this model in your scripts as follows:
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```python
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>>> from transformers import pipeline
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>>> import pandas as pd
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>>> ner = pipeline(task='ner', model='saattrupdan/nbailab-base-ner-scandi', aggregation_strategy='first')
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>>> result = ner('Borghild kjøper seg inn i Bunnpris')
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>>> pd.DataFrame.from_records(result)
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entity_group score word start end
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0 PER 0.981257 Borghild 0 8
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1 ORG 0.974099 Bunnpris 26 34
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```
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## Performance
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The following is the Micro-F1 NER performance on Scandinavian NER test datasets, compared with the current state-of-the-art. The models have been evaluated on the test set along with 9 bootstrapped versions of it, with the mean and 95% confidence interval shown here:
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