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[Doctests] Correct task summary #16644

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Apr 11, 2022
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[Doctests] Correct task summary #16644

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15 changes: 7 additions & 8 deletions docs/source/en/task_summary.mdx
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Expand Up @@ -1077,16 +1077,15 @@ The following examples demonstrate how to use a [`pipeline`] and a model and tok
>>> from transformers import pipeline

>>> vision_classifier = pipeline(task="image-classification")
>>> vision_classifier(
>>> result = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
[{'label': 'lynx, catamount', 'score': 0.4403027892112732},
{'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
'score': 0.03433405980467796},
{'label': 'snow leopard, ounce, Panthera uncia',
'score': 0.032148055732250214},
{'label': 'Egyptian cat', 'score': 0.02353910356760025},
{'label': 'tiger cat', 'score': 0.023034192621707916}]
>>> print("\n".join([f"Class {d['label']} with score {round(d['score'], 4)}" for d in result]))
Class lynx, catamount with score 0.4335
Class cougar, puma, catamount, mountain lion, painter, panther, Felis concolor with score 0.0348
Class snow leopard, ounce, Panthera uncia with score 0.0324
Class Egyptian cat with score 0.0239
Class tiger cat with score 0.0229
```

The general process for using a model and feature extractor for image classification is:
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