Add Hindi हिन्दी and Arabic العربية Docs translations (#6428)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/en/datasets/segment/coco.md

@@ -31,7 +31,7 @@ COCO-Seg is widely used for training and evaluating deep learning models in inst
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO-Seg dataset, the `coco.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco.yaml"
+!!! Example "ultralytics/cfg/datasets/coco.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco.yaml"
@@ -41,7 +41,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the COCO-Seg d
 
 If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/segment/coco8-seg.md

@@ -17,7 +17,7 @@ and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
 A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Seg dataset, the `coco8-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml).
 
-!!! example "ultralytics/cfg/datasets/coco8-seg.yaml"
+!!! Example "ultralytics/cfg/datasets/coco8-seg.yaml"
 
     ```yaml
     --8<-- "ultralytics/cfg/datasets/coco8-seg.yaml"
@@ -27,7 +27,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
-!!! example "Train Example"
+!!! Example "Train Example"
 
     === "Python"
 
@@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8-Seg
 
 If you use the COCO dataset in your research or development work, please cite the following paper:
 
-!!! note ""
+!!! Note ""
 
     === "BibTeX"
 

docs/en/datasets/segment/index.md

@@ -33,7 +33,7 @@ Here is an example of the YOLO dataset format for a single image with two object
 1 0.504 0.000 0.501 0.004 0.498 0.004 0.493 0.010 0.492 0.0104
 ```
 
-!!! tip "Tip"
+!!! Tip "Tip"
 
       - The length of each row does **not** have to be equal.
       - Each segmentation label must have a **minimum of 3 xy points**: `<class-index> <x1> <y1> <x2> <y2> <x3> <y3>`
@@ -66,7 +66,7 @@ The `train` and `val` fields specify the paths to the directories containing the
 
 ## Usage
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -101,7 +101,7 @@ If you have your own dataset and would like to use it for training segmentation
 
 You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -123,7 +123,7 @@ Auto-annotation is an essential feature that allows you to generate a segmentati
 
 To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
 
-!!! example ""
+!!! Example ""
 
     === "Python"