[doc] consistent True/False/None default format (#14951)

* [doc] consistent True/False/None default format

* Update src/transformers/models/xlnet/modeling_xlnet.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/segformer.mdx

@@ -57,13 +57,13 @@ Tips:
   important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
   such as 512x512 or 640x640, after which they are normalized.
 - One additional thing to keep in mind is that one can initialize [`SegformerFeatureExtractor`] with
-  `reduce_labels` set to *True* or *False*. In some datasets (like ADE20k), the 0 index is used in the annotated
+  `reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
   segmentation maps for background. However, ADE20k doesn't include the "background" class in its 150 labels.
   Therefore, `reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
   background class (i.e. it replaces 0 in the annotated maps by 255, which is the *ignore_index* of the loss function
   used by [`SegformerForSemanticSegmentation`]). However, other datasets use the 0 index as
   background class and include this class as part of all labels. In that case, `reduce_labels` should be set to
-  *False*, as loss should also be computed for the background class.
+  `False`, as loss should also be computed for the background class.
 - As most models, SegFormer comes in different sizes, the details of which can be found in the table below.
 
 | **Model variant** | **Depths**    | **Hidden sizes**    | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** |