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

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

Co-authored-by: John St John <jstjohn@users.noreply.huggingface.co>

Files changed (1) hide show

README.md CHANGED Viewed

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

 The following shows how one can predict masked passages using the different denoising strategies.
 Given the size of the model the following examples need to be run on at least a 40GB A100 GPU.
+### S-Denoising
+For *S-Denoising*, please make sure to prompt the text with the prefix `[S2S]` as shown below.
 ```python
 from transformers import T5ForConditionalGeneration, AutoTokenizer
 # -> <pad>. Dudley was a very good boy, but he was also very stupid.</s>
 ```
+### R-Denoising
+For *R-Denoising*, please make sure to prompt the text with the prefix `[NLU]` as shown below.
 ```python
 from transformers import T5ForConditionalGeneration, AutoTokenizer