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[docs] fix xref to PreTrainedModel.generate (#11049)

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* fix xref to generate

* do the same for search methods

* style

* style
This commit is contained in:
Stas Bekman
2021-06-02 09:21:05 -07:00
committed by GitHub
parent 123b597f5d
commit d406a2729a
6 changed files with 37 additions and 29 deletions
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2
docs/source/model_doc/bart.rst
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@@ -61,7 +61,7 @@ Implementation Notes
- Model predictions are intended to be identical to the original implementation when
:obj:`force_bos_token_to_be_generated=True`. This only works, however, if the string you pass to
:func:`fairseq.encode` starts with a space.
- :meth:`~transformers.BartForConditionalGeneration.generate` should be used for conditional generation tasks like
- :meth:`~transformers.generation_utils.GenerationMixin.generate` should be used for conditional generation tasks like
summarization, see the example in that docstrings.
- Models that load the `facebook/bart-large-cnn` weights will not have a :obj:`mask_token_id`, or be able to perform
mask-filling tasks.

8
docs/source/model_doc/t5.rst
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@@ -1,4 +1,4 @@
..
..
Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
@@ -44,9 +44,9 @@ Tips:
For more information about which prefix to use, it is easiest to look into Appendix D of the `paper
<https://arxiv.org/pdf/1910.10683.pdf>`__. - For sequence-to-sequence generation, it is recommended to use
:obj:`T5ForConditionalGeneration.generate()`. This method takes care of feeding the encoded input via cross-attention
layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative scalar embeddings.
Encoder input padding can be done on the left and on the right.
:meth:`~transformers.generation_utils.GenerationMixin.generate`. This method takes care of feeding the encoded input
via cross-attention layers to the decoder and auto-regressively generates the decoder output. - T5 uses relative
scalar embeddings. Encoder input padding can be done on the left and on the right.
This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
<https://github.com/google-research/text-to-text-transfer-transformer>`__.