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/integrations/comet.md

@@ -26,7 +26,7 @@ By combining Ultralytics YOLOv8 with Comet ML, you unlock a range of benefits. T
 
 To install the required packages, run:
 
-!!! tip "Installation"
+!!! Tip "Installation"
 
     === "CLI"
 
@@ -39,7 +39,7 @@ To install the required packages, run:
 
 After installing the required packages, you’ll need to sign up, get a [Comet API Key](https://www.comet.com/signup), and configure it.
 
-!!! tip "Configuring Comet ML"
+!!! Tip "Configuring Comet ML"
 
     === "CLI"
 
@@ -61,7 +61,7 @@ comet_ml.init(project_name="comet-example-yolov8-coco128")
 
 Before diving into the usage instructions, be sure to check out the range of [YOLOv8 models offered by Ultralytics](../models/index.md). This will help you choose the most appropriate model for your project requirements.
 
-!!! example "Usage"
+!!! Example "Usage"
 
     === "Python"
 

docs/en/integrations/mlflow.md

@@ -34,7 +34,7 @@ pip install mlflow
 
 Make sure that MLflow logging is enabled in Ultralytics settings. Usually, this is controlled by the settings `mflow` key. See the [settings](https://docs.ultralytics.com/quickstart/#ultralytics-settings) page for more info.
 
-!!! example "Update Ultralytics MLflow Settings"
+!!! Example "Update Ultralytics MLflow Settings"
 
     === "Python"
         Within the Python environment, call the `update` method on the `settings` object to change your settings:

docs/en/integrations/openvino.md

@@ -27,7 +27,7 @@ OpenVINO, short for Open Visual Inference & Neural Network Optimization toolkit,
 
 Export a YOLOv8n model to OpenVINO format and run inference with the exported model.
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 
@@ -101,7 +101,7 @@ For more detailed steps and code snippets, refer to the [OpenVINO documentation]
 
 YOLOv8 benchmarks below were run by the Ultralytics team on 4 different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX and OpenVINO. Benchmarks were run on Intel Flex and Arc GPUs, and on Intel Xeon CPUs at FP32 precision (with the `half=False` argument).
 
-!!! note
+!!! Note
 
     The benchmarking results below are for reference and might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run.
 
@@ -251,7 +251,7 @@ Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.
 
 To reproduce the Ultralytics benchmarks above on all export [formats](../modes/export.md) run this code:
 
-!!! example ""
+!!! Example ""
 
     === "Python"
 

docs/en/integrations/ray-tune.md

@@ -28,7 +28,7 @@ YOLOv8 also allows optional integration with [Weights & Biases](https://wandb.ai
 
 To install the required packages, run:
 
-!!! tip "Installation"
+!!! Tip "Installation"
 
     === "CLI"
 
@@ -42,7 +42,7 @@ To install the required packages, run:
 
 ## Usage
 
-!!! example "Usage"
+!!! Example "Usage"
 
     === "Python"
 
@@ -103,7 +103,7 @@ The following table lists the default search space parameters for hyperparameter
 
 In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLOv8. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.
 
-!!! example "Usage"
+!!! Example "Usage"
 
     ```python
     from ultralytics import YOLO

docs/en/integrations/roboflow.md

@@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Roboflow, vector analysis, confusion matrix, data
 
 [Roboflow](https://roboflow.com/?ref=ultralytics) has everything you need to build and deploy computer vision models. Connect Roboflow at any step in your pipeline with APIs and SDKs, or use the end-to-end interface to automate the entire process from image to inference. Whether you’re in need of [data labeling](https://roboflow.com/annotate?ref=ultralytics), [model training](https://roboflow.com/train?ref=ultralytics), or [model deployment](https://roboflow.com/deploy?ref=ultralytics), Roboflow gives you building blocks to bring custom computer vision solutions to your project.
 
-!!! warning
+!!! Warning
 
     Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.