update

README.md

@@ -2,8 +2,8 @@
 dataset_info:
 - config_name: train
   features:
-  - name: video 
-    dtype: binary
+  - name: video_path 
+    dtype: string
   - name: internal_id
     dtype: string  
   - name: prompt
@@ -93,13 +93,13 @@ dataset_info:
     dtype: string 
   - name: standard_answer
     dtype: string
-  - name: file1
-    dtype: binary
-  - name: file2
-    dtype: binary
+  - name: video1_path
+    dtype: string
+  - name: video2_path
+    dtype: string
   splits:
   - name: regression
-    num_examples: 2260
+    num_examples: 1795
 
 - config_name: monetbench
   features:
@@ -109,10 +109,10 @@ dataset_info:
     dtype: string 
   - name: standard_answer
     dtype: string
-  - name: file1
-    dtype: binary
-  - name: file2
-    dtype: binary
+  - name: video1_path
+    dtype: string
+  - name: video2_path
+    dtype: string
   splits:
   - name: monetbench
     num_examples: 1000
@@ -230,11 +230,17 @@ The `meta_mask` feature is used for balanced sampling during model training:
 - Elements with value 0 indicate that the corresponding binary judgment was ignored during training
 
 ## Data Processing
+```bash
+cd videos
+tar -xvzf train.tar.gz
+tar -xvzf regression.tar.gz
+tar -xvzf monetbench.tar.gz
+```
 
 We provide `extract.py` for processing the `train` dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.
 
 ```bash
-python extract.py [--save_imgs] [--process_qa]
+python extract.py
 ```
 
 ## Citation Information

extract.py

@@ -14,27 +14,22 @@ def load_questions_from_meta_qa(meta_qa_file):
         questions = [line.strip() for line in f if line.strip()]
     return questions
 
-def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None, output_videos=None, process_qa=False):
+def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None):
     """
-    Process Parquet files to generate a JSONL file with optional video export and QA list creation.
+    Process Parquet files to generate a JSONL file with QA list creation.
     
     Args:
         data_dir (str): Directory containing Parquet files.
         output_jsonl (str): Output JSONL file path.
         meta_qa_file (str, optional): Path to the meta_qa_en.txt file for QA list creation.
-        output_videos (str, optional): Directory path to save videos. If None, videos are not saved.
-        process_qa (bool): Whether to process and include QA pairs in the output.
     
     Returns:
         None
     """
 
-    if output_videos and not os.path.exists(output_videos):
-        os.makedirs(output_videos)
-
-    # Load questions only if QA processing is enabled
+    # Load questions if meta_qa_file is provided
     questions = None
-    if process_qa and meta_qa_file:
+    if meta_qa_file:
         questions = load_questions_from_meta_qa(meta_qa_file)
 
     jsonl_data = []
@@ -43,31 +38,20 @@ def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None, output_vide
 
     for parquet_file in parquet_files:
         dataset = Dataset.from_parquet(parquet_file)
-        
+
         for row in dataset:
             json_item = {
                 "internal_id": row["internal_id"],
                 "url": row["url"],
-                "prompt":row["prompt"],
+                "video_path": row["video_path"],
+                "prompt": row["prompt"],
                 "annotation": row["annotation"],
                 "meta_result": row["meta_result"],
                 "meta_mask": row["meta_mask"],
             }
-            
-            # Optionally save videos
-            if output_videos:
-                video_data = row["video"]  
-                video_path = os.path.join(output_videos, f"{row['internal_id']}.mp4")  
-                
-                try:
-                    with open(video_path, "wb") as video_file:
-                        video_file.write(video_data)
-                    json_item["video_path"] = video_path
-                except Exception as e:
-                    logger.error(f"Error saving video for internal_id {row['internal_id']}: {e}")
-            
-            # Optionally process QA pairs
-            if process_qa and questions:
+
+            # Process QA pairs if questions are provided
+            if questions:
                 qa_list = []
                 meta_result = row["meta_result"]
                 meta_mask = row["meta_mask"]
@@ -88,20 +72,14 @@ def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None, output_vide
     logger.info(f"Finished writing JSONL file with {len(jsonl_data)} items.")
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert Video dataset Parquet files to JSONL format with optional video extraction and QA list generation.")
+    parser = argparse.ArgumentParser(description="Convert Video dataset Parquet files to JSONL format with QA list generation.")
     parser.add_argument("--data_dir", type=str, default='train', help="Directory containing Parquet files.")
     parser.add_argument("--output_jsonl", type=str, default='annotation.jsonl', help="Path to the output JSONL file.")
     parser.add_argument("--meta_qa_file", type=str, default="meta_qa_en.txt", help="Optional: Path to the meta_qa_en.txt file for QA list generation.")
-    parser.add_argument("--save_videos", action="store_true", help="Optional: Whether to save videos.")
-    parser.add_argument("--process_qa", action="store_true", help="Optional: Process and include QA pairs in the output.")
     args = parser.parse_args()
 
-    output_videos = 'videos' if args.save_videos else None
-    
     process_parquet_files(
         data_dir=args.data_dir,
         output_jsonl=args.output_jsonl,
-        meta_qa_file=args.meta_qa_file,
-        output_videos=output_videos,
-        process_qa=args.process_qa
+        meta_qa_file=args.meta_qa_file
     )

monetbench/{test-00000-of-00005.parquet → test-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
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+size 96016

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