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[docs] navigation improvement between text gen pipelines and text gen params (#26477)

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* navigation improvement between text generation pipelines and text generation docs

* make style
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Maria Khalusova 2023-09-29 03:43:39 -04:00 committed by GitHub
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3
docs/source/en/generation_strategies.md
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@ -82,7 +82,8 @@ Even if the default decoding strategy mostly works for your task, you can still
commonly adjusted parameters include:
- `max_new_tokens`: the maximum number of tokens to generate. In other words, the size of the output sequence, not
including the tokens in the prompt.
including the tokens in the prompt. As an alternative to using the output's length as a stopping criteria, you can choose
to stop generation whenever the full generation exceeds some amount of time. To learn more, check [`StoppingCriteria`].
- `num_beams`: by specifying a number of beams higher than 1, you are effectively switching from greedy search to
beam search. This strategy evaluates several hypotheses at each time step and eventually chooses the hypothesis that
has the overall highest probability for the entire sequence. This has the advantage of identifying high-probability

6
src/transformers/pipelines/text2text_generation.py
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@ -39,8 +39,10 @@ class Text2TextGenerationPipeline(Pipeline):
[{'generated_text': 'question: Who created the RuPERTa-base?'}]
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial). You can pass text
generation parameters to this pipeline to control stopping criteria, decoding strategy, and more. Learn more about
text generation parameters in [Text generation strategies](../generation_strategies) and [Text
generation](text_generation).
This Text2TextGenerationPipeline pipeline can currently be loaded from [`pipeline`] using the following task
identifier: `"text2text-generation"`.

5
src/transformers/pipelines/text_generation.py
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@ -39,7 +39,10 @@ class TextGenerationPipeline(Pipeline):
>>> outputs = generator("My tart needs some", num_return_sequences=4, return_full_text=False)
```
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial). You can pass text
generation parameters to this pipeline to control stopping criteria, decoding strategy, and more. Learn more about
text generation parameters in [Text generation strategies](../generation_strategies) and [Text
generation](text_generation).
This language generation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
`"text-generation"`.