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/tasks/classify.md

@@ -12,7 +12,7 @@ Image classification is the simplest of the three tasks and involves classifying
 
 The output of an image classifier is a single class label and a confidence score. Image classification is useful when you need to know only what class an image belongs to and don't need to know where objects of that class are located or what their exact shape is.
 
-!!! tip "Tip"
+!!! Tip "Tip"
 
     YOLOv8 Classify models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml).
 
@@ -40,7 +40,7 @@ YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose model
 
 Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -77,7 +77,7 @@ YOLO classification dataset format can be found in detail in the [Dataset Guide]
 
 Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -104,7 +104,7 @@ Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument
 
 Use a trained YOLOv8n-cls model to run predictions on images.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -131,7 +131,7 @@ See full `predict` mode details in the [Predict](https://docs.ultralytics.com/mo
 
 Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/tasks/detect.md

@@ -23,7 +23,7 @@ The output of an object detector is a set of bounding boxes that enclose the obj
   <strong>Watch:</strong> Object Detection with Pre-trained Ultralytics YOLOv8 Model.
 </p>
 
-!!! tip "Tip"
+!!! Tip "Tip"
 
     YOLOv8 Detect models are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
 
@@ -51,7 +51,7 @@ YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models
 
 Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -87,7 +87,7 @@ YOLO detection dataset format can be found in detail in the [Dataset Guide](../d
 
 Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -116,7 +116,7 @@ Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need
 
 Use a trained YOLOv8n model to run predictions on images.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -143,7 +143,7 @@ See full `predict` mode details in the [Predict](https://docs.ultralytics.com/mo
 
 Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/tasks/pose.md

@@ -24,7 +24,7 @@ The output of a pose estimation model is a set of points that represent the keyp
   <strong>Watch:</strong> Pose Estimation with Ultralytics YOLOv8.
 </p>
 
-!!! tip "Tip"
+!!! Tip "Tip"
 
     YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt`. These models are trained on the [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks.
 
@@ -54,7 +54,7 @@ YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models ar
 
 Train a YOLOv8-pose model on the COCO128-pose dataset.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -91,7 +91,7 @@ YOLO pose dataset format can be found in detail in the [Dataset Guide](../datase
 Validate trained YOLOv8n-pose model accuracy on the COCO128-pose dataset. No argument need to passed as the `model`
 retains it's training `data` and arguments as model attributes.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -120,7 +120,7 @@ retains it's training `data` and arguments as model attributes.
 
 Use a trained YOLOv8n-pose model to run predictions on images.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -147,7 +147,7 @@ See full `predict` mode details in the [Predict](https://docs.ultralytics.com/mo
 
 Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/tasks/segment.md

@@ -23,7 +23,7 @@ The output of an instance segmentation model is a set of masks or contours that
   <strong>Watch:</strong> Run Segmentation with Pre-Trained Ultralytics YOLOv8 Model in Python.
 </p>
 
-!!! tip "Tip"
+!!! Tip "Tip"
 
     YOLOv8 Segment models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
 
@@ -51,7 +51,7 @@ YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models
 
 Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -88,7 +88,7 @@ YOLO segmentation dataset format can be found in detail in the [Dataset Guide](.
 Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`
 retains it's training `data` and arguments as model attributes.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -121,7 +121,7 @@ retains it's training `data` and arguments as model attributes.
 
 Use a trained YOLOv8n-seg model to run predictions on images.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -148,7 +148,7 @@ See full `predict` mode details in the [Predict](https://docs.ultralytics.com/mo
 
 Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
 
-!!! example ""
+!!! Example ""
 
     === "Python"