[Docs] Model_doc structure/clarity improvements (#26876)

* first batch of structure improvements for model_docs

* second batch of structure improvements for model_docs

* more structure improvements for model_docs

* more structure improvements for model_docs

* structure improvements for cv model_docs

* more structural refactoring

* addressed feedback about image processors

docs/source/en/model_doc/xlm-roberta.md

@@ -46,16 +46,14 @@ languages at scale. Finally, we show, for the first time, the possibility of mul
 per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
 will make XLM-R code, data, and models publicly available.*
 
-Tips:
+This model was contributed by [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).
+
+## Usage tips
 
 - XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
   not require `lang` tensors to understand which language is used, and should be able to determine the correct
   language from the input ids.
 - Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective. It only uses masked language modeling on sentences coming from one language.
-- This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples
-  as well as the information relative to the inputs and outputs.
-
-This model was contributed by [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).
 
 ## Resources
 
@@ -110,6 +108,11 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 - A blog post on how to [Deploy Serverless XLM RoBERTa on AWS Lambda](https://www.philschmid.de/multilingual-serverless-xlm-roberta-with-huggingface).
 
+<Tip> 
+
+This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples as well as the information relative to the inputs and outputs.
+</Tip>
+
 ## XLMRobertaConfig
 
 [[autodoc]] XLMRobertaConfig
@@ -126,6 +129,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] XLMRobertaTokenizerFast
 
+<frameworkcontent>
+<pt>
+
 ## XLMRobertaModel
 
 [[autodoc]] XLMRobertaModel
@@ -161,6 +167,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] XLMRobertaForQuestionAnswering
     - forward
 
+</pt>
+<tf>
+
 ## TFXLMRobertaModel
 
 [[autodoc]] TFXLMRobertaModel
@@ -196,6 +205,9 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 [[autodoc]] TFXLMRobertaForQuestionAnswering
     - call
 
+</tf>
+<jax>
+
 ## FlaxXLMRobertaModel
 
 [[autodoc]] FlaxXLMRobertaModel
@@ -230,3 +242,6 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 [[autodoc]] FlaxXLMRobertaForQuestionAnswering
     - __call__
+
+</jax>
+</frameworkcontent>