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Docs: fix generate-related rendering issues (#30600)

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* fix the other generate links

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This commit is contained in:
Joao Gante
2024-05-02 14:42:25 +01:00
committed by GitHub
parent 801894e08c
commit aa55ff44a2
13 changed files with 31 additions and 37 deletions
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2
docs/source/en/generation_strategies.md
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@@ -21,7 +21,7 @@ more. It also plays a role in a variety of mixed-modality applications that have
and vision-to-text. Some of the models that can generate text include
GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper.
Check out a few examples that use [`~transformers.generation_utils.GenerationMixin.generate`] method to produce
Check out a few examples that use [`~generation.GenerationMixin.generate`] method to produce
text outputs for different tasks:
* [Text summarization](./tasks/summarization#inference)
* [Image captioning](./model_doc/git#transformers.GitForCausalLM.forward.example)

2
docs/source/en/tasks/language_modeling.md
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@@ -382,7 +382,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
>>> inputs = tokenizer(prompt, return_tensors="pt").input_ids
```
Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to generate text.
Use the [`~generation.GenerationMixin.generate`] method to generate text.
For more details about the different text generation strategies and parameters for controlling generation, check out the [Text generation strategies](../generation_strategies) page.
```py

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docs/source/en/tasks/summarization.md
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@@ -355,7 +355,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
>>> inputs = tokenizer(text, return_tensors="pt").input_ids
```
Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
Use the [`~generation.GenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
```py
>>> from transformers import AutoModelForSeq2SeqLM

2
docs/source/en/tasks/translation.md
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@@ -364,7 +364,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors:
>>> inputs = tokenizer(text, return_tensors="pt").input_ids
```
Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
Use the [`~generation.GenerationMixin.generate`] method to create the translation. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](../main_classes/text_generation) API.
```py
>>> from transformers import AutoModelForSeq2SeqLM