updated script

README.md

@@ -38,6 +38,39 @@ You can load the dataset using the `datasets` library from Hugging Face:
 from datasets import load_dataset
 
 dataset = load_dataset("QCRI/ArMeme")
+
+# Specify the directory where you want to save the dataset
+output_dir="./ArMeme/"
+
+# Save the dataset to the specified directory. This will save all splits to the output directory.
+dataset.save_to_disk(output_dir)
+
+# If you want to get the raw images from HF dataset format
+
+from PIL import Image
+import os
+import json
+
+# Directory to save the images
+output_dir="./ArMeme/"
+os.makedirs(output_dir, exist_ok=True)
+
+# Assuming the images are in a column named "image"
+image_column = "image"
+
+# Iterate over the dataset and save each image
+for split in ['train','dev','test']:     
+    jsonl_path = os.path.join(output_dir, f"arabic_memes_categorization_{split}.jsonl")
+    with open(jsonl_path, 'w', encoding='utf-8') as f:    
+        for idx, item in enumerate(dataset[split]):
+            # Access the image directly as it's already a PIL.Image object
+            image = item[image_column]
+            image_path = os.path.join(output_dir, item['img_path'])
+            # Ensure the directory exists
+            os.makedirs(os.path.dirname(image_path), exist_ok=True)
+            image.save(image_path)
+            del item['image']
+            f.write(json.dumps(item, ensure_ascii=False) + '\n')
 ```
 
 **Language:** Arabic