[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/tapas.md

@@ -44,7 +44,7 @@ alt="drawing" width="600"/>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The Tensorflow version of this model was contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/tapas).
 
-Tips:
+## Usage tips
 
 - TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows you to encode longer sequences without running out of embeddings. This is reflected in the `reset_position_index_per_cell` parameter of [`TapasConfig`], which is set to `True` by default. The default versions of the models available on the [hub](https://huggingface.co/models?search=tapas) all use relative position embeddings. You can still use the ones with absolute position embeddings by passing in an additional argument `revision="no_reset"` when calling the `from_pretrained()` method. Note that it's usually advised to pad the inputs on the right rather than the left.
 - TAPAS is based on BERT, so `TAPAS-base` for example corresponds to a `BERT-base` architecture. Of course, `TAPAS-large` will result in the best performance (the results reported in the paper are from `TAPAS-large`). Results of the various sized models are shown on the [original Github repository](https://github.com/google-research/tapas>).
@@ -573,7 +573,7 @@ Predicted answer: SUM > 87, 53, 69
 
 In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such that the `prev_labels` token types can be overwritten by the predicted `labels` of the previous table-question pair. Again, more info can be found in [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for PyTorch) and [this notebook](https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for TensorFlow).
 
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Masked language modeling task guide](../tasks/masked_language_modeling)
@@ -590,6 +590,9 @@ In case of a conversational set-up, then each table-question pair must be provid
     - convert_logits_to_predictions
     - save_vocabulary
 
+<frameworkcontent>
+<pt>
+
 ## TapasModel
 [[autodoc]] TapasModel
     - forward
@@ -606,6 +609,9 @@ In case of a conversational set-up, then each table-question pair must be provid
 [[autodoc]] TapasForQuestionAnswering
     - forward
 
+</pt>
+<tf>
+
 ## TFTapasModel
 [[autodoc]] TFTapasModel
     - call
@@ -620,4 +626,9 @@ In case of a conversational set-up, then each table-question pair must be provid
     
 ## TFTapasForQuestionAnswering
 [[autodoc]] TFTapasForQuestionAnswering
-    - call
+    - call
+
+</tf>
+</frameworkcontent>
+
+