docs(readme): document extraction script usage

README.md

@@ -146,4 +146,46 @@ ds = load_dataset("lucabaggi/animal-wildlife")
 
 ## How the data was generated
 
-You can find the script [here](./extract.py)
+You can find the source code for the extraction pipeline [here](./extract.py). Note: partly generated with Claude3 and Codestral 😎😅 Please feel free to open an issue in the discussion sction if you wish to improve the code.
+
+```
+$ uv run --python=3.11 -- python -m extract --help
+
+usage: extract.py [-h] [--destination-dir DESTINATION_DIR] [--split-ratio SPLIT_RATIO] [--random-seed RANDOM_SEED] [--remove-zip] zip_file
+
+Reorganize dataset.
+
+positional arguments:
+  zip_file              Path to the zip file.
+
+options:
+  -h, --help            show this help message and exit
+  --destination-dir DESTINATION_DIR
+                        Path to the destination directory.
+  --split-ratio SPLIT_RATIO
+                        Ratio of data to be used for training.
+  --random-seed RANDOM_SEED
+                        Random seed for reproducibility.
+  --remove-zip          Whether to remove the source zip archive file after extraction.
+```
+
+Example usage:
+
+1. Download the data from Kaggle. You can use Kaggle Python SDK, but that might require an API key if you use it locally.
+
+2. Invoke the script:
+
+```bash
+uv run --python=3.11 -- python -m extract -- archive.zip
+```
+
+This will explode the contents of the zip archive into a `data` directory, splitting the train and test dataset in a 80%/20% ratio.
+
+3. Upload to the hub:
+
+```python
+from datasets import load_dataset
+
+ds = load_datset("imagefolder", data_dir="data")
+ds.push_to_hub()
+```