[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/prophetnet.md

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-
-**DISCLAIMER:** If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title) and assign
-@patrickvonplaten
-
 ## Overview
 
 The ProphetNet model was proposed in [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training,](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei
@@ -49,15 +45,15 @@ dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Giga
 abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
 state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
 
-Tips:
+The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).
+
+## Usage tips
 
 - ProphetNet is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
   the left.
 - The model architecture is based on the original Transformer, but replaces the “standard” self-attention mechanism in the decoder by a a main self-attention mechanism and a self and n-stream (predict) self-attention mechanism.
 
-The Authors' code can be found [here](https://github.com/microsoft/ProphetNet).
-
-## Documentation resources
+## Resources
 
 - [Causal language modeling task guide](../tasks/language_modeling)
 - [Translation task guide](../tasks/translation)