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Documentation fixes (#20607)

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Samuel Xu
2022-12-06 07:32:46 -05:00
committed by GitHub
parent 28f3d431d4
commit e842e181df
1 changed files with 4 additions and 4 deletions
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8
docs/source/en/main_classes/pipelines.mdx
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@@ -41,19 +41,19 @@ the hub already defines it:
```python ```python
>>> pipe = pipeline(model="roberta-large-mnli") >>> pipe = pipeline(model="roberta-large-mnli")
>>> pipe("This restaurant is awesome") >>> pipe("This restaurant is awesome")
[{'label': 'POSITIVE', 'score': 0.9998743534088135}] [{'label': 'NEUTRAL', 'score': 0.7313136458396912}]
``` ```
To call a pipeline on many items, you can either call with a *list*. To call a pipeline on many items, you can call it with a *list*.
```python ```python
>>> pipe = pipeline("text-classification") >>> pipe = pipeline("text-classification")
>>> pipe(["This restaurant is awesome", "This restaurant is aweful"]) >>> pipe(["This restaurant is awesome", "This restaurant is awful"])
[{'label': 'POSITIVE', 'score': 0.9998743534088135}, [{'label': 'POSITIVE', 'score': 0.9998743534088135},
{'label': 'NEGATIVE', 'score': 0.9996669292449951}] {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
``` ```
To iterate of full datasets it is recommended to use a `dataset` directly. This means you don't need to allocate To iterate over full datasets it is recommended to use a `dataset` directly. This means you don't need to allocate
the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
GPU. If it doesn't don't hesitate to create an issue. GPU. If it doesn't don't hesitate to create an issue.