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README.md
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@@ -35,9 +35,6 @@ You can then use this pipeline to classify sequences into any of the class names
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sequence_to_classify = "one day I will see the world"
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candidate_labels = ['travel', 'cooking', 'dancing']
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classifier(sequence_to_classify, candidate_labels)
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#{'sequence': 'one day I will see the world',
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# 'labels': ['travel', 'cooking', 'dancing'],
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# 'scores': [0.7728410363197327, 0.13207288086414337, 0.09508601576089859]}
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```
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If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
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```python
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candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
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classifier(sequence_to_classify, candidate_labels, multi_class=True)
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#{'sequence': 'one day I will see the world',
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# 'labels': ['exploration', 'travel', 'cooking', 'dancing'],
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# 'scores': [0.9920766353607178,
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# 0.7247188091278076,
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# 0.08411424607038498,
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# 0.03875880688428879]}
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```
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### Eval results
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sequence_to_classify = "one day I will see the world"
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candidate_labels = ['travel', 'cooking', 'dancing']
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classifier(sequence_to_classify, candidate_labels)
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```
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If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
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```python
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candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
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classifier(sequence_to_classify, candidate_labels, multi_class=True)
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```
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### Eval results
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