[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/xmod.md

@@ -25,13 +25,15 @@ The abstract from the paper is the following:
 
 *Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. In contrast with prior work that learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-MOD) models from the start. Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance. Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.*
 
+This model was contributed by [jvamvas](https://huggingface.co/jvamvas).
+The original code can be found [here](https://github.com/facebookresearch/fairseq/tree/58cc6cca18f15e6d56e3f60c959fe4f878960a60/fairseq/models/xmod) and the original documentation is found [here](https://github.com/facebookresearch/fairseq/tree/58cc6cca18f15e6d56e3f60c959fe4f878960a60/examples/xmod).
+
+## Usage tips
+
 Tips:
 - X-MOD is similar to [XLM-R](xlm-roberta), but a difference is that the input language needs to be specified so that the correct language adapter can be activated.
 - The main models – base and large – have adapters for 81 languages.
 
-This model was contributed by [jvamvas](https://huggingface.co/jvamvas).
-The original code can be found [here](https://github.com/facebookresearch/fairseq/tree/58cc6cca18f15e6d56e3f60c959fe4f878960a60/fairseq/models/xmod) and the original documentation is found [here](https://github.com/facebookresearch/fairseq/tree/58cc6cca18f15e6d56e3f60c959fe4f878960a60/examples/xmod).
-
 ## Adapter Usage
 
 ### Input language

docs/source/en/model_doc/yolos.md

@@ -25,10 +25,6 @@ The abstract from the paper is the following:
 
 *Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.*
 
-Tips:
-
-- One can use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
-
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
 alt="drawing" width="600"/>
 
@@ -47,6 +43,12 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
 
 If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 
+<Tip>
+
+Use [`YolosImageProcessor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
+
+</Tip>
+
 ## YolosConfig
 
 [[autodoc]] YolosConfig
@@ -65,13 +67,11 @@ If you're interested in submitting a resource to be included here, please feel f
     - pad
     - post_process_object_detection
 
-
 ## YolosModel
 
 [[autodoc]] YolosModel
     - forward
 
-
 ## YolosForObjectDetection
 
 [[autodoc]] YolosForObjectDetection

docs/source/en/model_doc/yoso.md

@@ -37,7 +37,9 @@ length where we see favorable performance relative to a standard pretrained Tran
 for evaluating performance on long sequences, our method achieves results consistent with softmax self-attention but with sizable 
 speed-ups and memory savings and often outperforms other efficient self-attention methods. Our code is available at this https URL*
 
-Tips:
+This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/YOSO).
+
+## Usage tips
 
 - The YOSO attention algorithm is implemented through custom CUDA kernels, functions written in CUDA C++ that can be executed multiple times
 in parallel on a GPU.
@@ -52,9 +54,7 @@ alt="drawing" width="600"/>
 
 <small> YOSO Attention Algorithm. Taken from the <a href="https://arxiv.org/abs/2111.09714">original paper</a>.</small>
 
-This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/YOSO).
-
-## Documentation resources
+## Resources
 
 - [Text classification task guide](../tasks/sequence_classification)
 - [Token classification task guide](../tasks/token_classification)
@@ -66,19 +66,16 @@ This model was contributed by [novice03](https://huggingface.co/novice03). The o
 
 [[autodoc]] YosoConfig
 
-
 ## YosoModel
 
 [[autodoc]] YosoModel
     - forward
 
-
 ## YosoForMaskedLM
 
 [[autodoc]] YosoForMaskedLM
     - forward
 
-
 ## YosoForSequenceClassification
 
 [[autodoc]] YosoForSequenceClassification
@@ -89,13 +86,11 @@ This model was contributed by [novice03](https://huggingface.co/novice03). The o
 [[autodoc]] YosoForMultipleChoice
     - forward
 
-
 ## YosoForTokenClassification
 
 [[autodoc]] YosoForTokenClassification
     - forward
 
-
 ## YosoForQuestionAnswering
 
 [[autodoc]] YosoForQuestionAnswering