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README.md
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@@ -20,7 +20,7 @@ You can use this model directly for named entity recognition like so
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```python
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>>> from transformers import pipeline
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>>> ner = pipeline('ner', model='winberto-ner-uncased')
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>>> tokens = ner(
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>>> for t in toks:
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>>> print(f"{t['word']}: {t['entity_group']}: {t['score']:.5}")
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```python
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>>> from transformers import pipeline
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>>> ner = pipeline('ner', model='winberto-ner-uncased')
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>>> tokens = ner("Heitz Cabernet Sauvignon California Napa Valley Napa US this tremendous 100% varietal wine hails from oakville and was aged over three years in oak. juicy red-cherry fruit and a compelling hint of caramel greet the palate, framed by elegant, fine tannins and a subtle minty tone in the background. balanced and rewarding from start to finish, it has years ahead of it to develop further nuance. enjoy 2022")
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>>> for t in toks:
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>>> print(f"{t['word']}: {t['entity_group']}: {t['score']:.5}")
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