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Add Docs glossary links (#16448)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/usage/cli.md
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@ -35,7 +35,7 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
=== "Train"
Train a detection model for 10 epochs with an initial learning_rate of 0.01
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
```
@ -109,7 +109,7 @@ Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full
## Val
Validate trained YOLOv8n model accuracy on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
@ -221,7 +221,7 @@ This will create `default_copy.yaml`, which you can then pass as `cfg=default_co
### How do I use the Ultralytics YOLOv8 command line interface (CLI) for model training?
To train a YOLOv8 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a learning rate of 0.01, you would run:
To train a YOLOv8 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01, you would run:
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
@ -241,7 +241,7 @@ Each task can be customized with various arguments. For detailed syntax and exam
### How can I validate the accuracy of a trained YOLOv8 model using the CLI?
To validate a YOLOv8 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a batch size of 1 and image size of 640, run:
To validate a YOLOv8 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and image size of 640, run:
```bash
yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640