[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/convbert.md

@@ -44,12 +44,14 @@ ConvBERT significantly outperforms BERT and its variants in various downstream t
 fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while
 using less than 1/4 training cost. Code and pre-trained models will be released.*
 
-ConvBERT training tips are similar to those of BERT.
-
 This model was contributed by [abhishek](https://huggingface.co/abhishek). The original implementation can be found
 here: https://github.com/yitu-opensource/ConvBert
 
-## Documentation resources
+## Usage tips
+
+ConvBERT training tips are similar to those of BERT. For usage tips refer to [BERT documentation](bert).
+
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
@@ -73,6 +75,9 @@ here: https://github.com/yitu-opensource/ConvBert
 
 [[autodoc]] ConvBertTokenizerFast
 
+<frameworkcontent>
+<pt>
+
 ## ConvBertModel
 
 [[autodoc]] ConvBertModel
@@ -103,6 +108,9 @@ here: https://github.com/yitu-opensource/ConvBert
 [[autodoc]] ConvBertForQuestionAnswering
     - forward
 
+</pt>
+<tf>
+
 ## TFConvBertModel
 
 [[autodoc]] TFConvBertModel
@@ -132,3 +140,6 @@ here: https://github.com/yitu-opensource/ConvBert
 
 [[autodoc]] TFConvBertForQuestionAnswering
     - call
+
+</tf>
+</frameworkcontent>