patrickvonplaten
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Fix R vs S denoising documentation swap. (#2)

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- Fix R vs S denoising documentation swap. (71c34c5c6e3e54c5bf2cee360c3edfaa42acc1b3)


Co-authored-by: John St John

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  1. README.md +4 -4
README.md CHANGED
@@ -99,9 +99,9 @@ This model was contributed by [Daniel Hesslow](https://huggingface.co/Seledorn).
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  The following shows how one can predict masked passages using the different denoising strategies.
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  Given the size of the model the following examples need to be run on at least a 40GB A100 GPU.
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- ### R-Denoising
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- For *R-Denoising*, please make sure to prompt the text with the prefix `[S2S]` as shown below.
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  ```python
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  from transformers import T5ForConditionalGeneration, AutoTokenizer
@@ -120,9 +120,9 @@ print(tokenizer.decode(outputs[0]))
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  # -> <pad>. Dudley was a very good boy, but he was also very stupid.</s>
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  ```
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- ### S-Denoising
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- For *S-Denoising*, please make sure to prompt the text with the prefix `[NLU]` as shown below.
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  ```python
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  from transformers import T5ForConditionalGeneration, AutoTokenizer
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  The following shows how one can predict masked passages using the different denoising strategies.
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  Given the size of the model the following examples need to be run on at least a 40GB A100 GPU.
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+ ### S-Denoising
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+ For *S-Denoising*, please make sure to prompt the text with the prefix `[S2S]` as shown below.
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  ```python
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  from transformers import T5ForConditionalGeneration, AutoTokenizer
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  # -> <pad>. Dudley was a very good boy, but he was also very stupid.</s>
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  ```
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+ ### R-Denoising
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+ For *R-Denoising*, please make sure to prompt the text with the prefix `[NLU]` as shown below.
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  ```python
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  from transformers import T5ForConditionalGeneration, AutoTokenizer