Upload armeme_loader.py with huggingface_hub

armeme_loader.py

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+import os
+import json
+import datasets
+from datasets import Dataset, DatasetDict, load_dataset, Features, Value, Image
+
+# Define the paths to your dataset
+image_root_dir = "./"
+train_jsonl_file_path = "arabic_memes_categorization_train.jsonl"
+dev_jsonl_file_path = "arabic_memes_categorization_dev.jsonl"
+test_jsonl_file_path = "arabic_memes_categorization_test.jsonl"
+
+# Define features for the dataset
+features = Features({
+    'id': Value('string'),
+    'text': Value('string'),
+    'image': Image(),
+    'img_path': Value('string')
+})
+
+# Function to load each dataset split
+def load_armeme_split(jsonl_file_path, image_root_dir):
+    data = []
+    
+    # Load JSONL file
+    with open(jsonl_file_path, 'r') as f:
+        for line in f:
+            item = json.loads(line)
+            # Update image path to absolute path
+            item['img_path'] = os.path.join(image_root_dir, item['img_path'])
+            data.append(item)
+    
+    # Create a Hugging Face dataset
+    dataset = Dataset.from_dict(data, features=features)
+    return dataset
+
+# Load each split
+train_dataset = load_armeme_split(train_jsonl_file_path, image_root_dir)
+dev_dataset = load_armeme_split(dev_jsonl_file_path, image_root_dir)
+test_dataset = load_armeme_split(test_jsonl_file_path, image_root_dir)
+
+# Create a DatasetDict
+dataset_dict = DatasetDict({
+    'train': train_dataset,
+    'dev': dev_dataset,
+    'test': test_dataset
+})
+
+# Push the dataset to Hugging Face Hub
+dataset_dict.push_to_hub("QCRI/ArMeme", license="CC-By-NC-SA-4.0")