Upload data_to_llamafactory.py with huggingface_hub

data_to_llamafactory.py

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+# turn the data in /fs-computility/niuyazhe/lixueyan/meme/dataset-meme-rewardmodel/allmeme_nocross/allmeme_nocross_train.jsonl
+# to the format in /fs-computility/niuyazhe/lixueyan/meme/LLaMA-Factory/data/binary_class_demo.json
+
+import json
+import jsonlines
+import os
+
+output_path_root = '/fs-computility/niuyazhe/lixueyan/meme/LLaMA-Factory/data/'
+
+with open('/fs-computility/niuyazhe/lixueyan/meme/dataset-meme-rewardmodel/allmeme_nocross/allmeme_nocross_train.jsonl', 'r') as f:
+    dataset_dict = json.load(f)
+    for dataset_name, dataset in dataset_dict.items():
+        dataset_path = dataset['annotation']
+        dataset_name = dataset_path.split('/')[-1].split('.')[0]
+        # dataset_path is also jsonl file
+        with open(dataset_path, 'r') as file:
+            for item in jsonlines.Reader(file):
+                # check if the image exists
+                if not os.path.exists(item['image'][0]) or not os.path.exists(item['image'][1]):
+                    print(f"image {item['image'][0]} or {item['image'][1]} does not exist, skip this item")
+                    # skip the whole item
+                    continue
+
+                new_item = {}
+                new_item['messages'] = [
+                    {
+                        'role': 'user',
+                        'content': item['conversations'][0]['value']
+                    },
+                    {
+                        'role': 'assistant',
+                        'content': ''
+                    }
+                ]
+                new_item['images'] = item['image']
+                new_item['labels'] = [item['conversations'][1]['value']]
+                with open(output_path_root + dataset_name + '.json', 'a') as f:
+                    json.dump(new_item, f)
+                    f.write('\n')