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LICENSE

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+MIT License
+
+Copyright (c) [year] [fullname]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,65 @@
----
-license: mit
----
+---
+language:
+- en
+license: mit
+pretty_name: VCRBench
+---
+
+# VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models
+
+<a href='https://arxiv.org/abs/2505.08455'><img src='https://img.shields.io/badge/arXiv-paper-red'></a> 
+<a href='https://pritamqu.github.io/VCRBench/'><img src='https://img.shields.io/badge/project-VCRBench-blue'></a> 
+<a href='https://huggingface.co/datasets/pritamqu/VCRBench'><img src='https://img.shields.io/badge/huggingface-datasets-green'></a> 
+<a href='https://github.com/pritamqu/VCRBench'><img src='https://img.shields.io/badge/github-repository-purple'></a> 
+<a href='https://pritamqu.github.io/VCRBench/#leaderboard'><img src='https://img.shields.io/badge/vcrbench-leaderboard-yellow'></a> 
+
+
+Authors: [Pritam Sarkar](https://pritamsarkar.com) and [Ali Etemad](https://www.aiimlab.com/ali-etemad)
+
+This repository provides the official implementation of **[VCRBench](https://arxiv.org/abs/2505.08455)**.
+
+![](./sample.png)
+
+## Usage
+
+```
+from dataset import VCRBench
+dataset=VCRBench(question_file="pp.json", 
+                video_root="./",
+                mode='video', 
+                )
+    
+for sample in dataset:
+    print(sample['question'], )
+    print(sample['answer'], )
+    print('*'*10)
+
+    break
+
+```
+
+
+### Licensing Information
+
+This dataset incorporates samples from [CrossTask](https://github.com/DmZhukov/CrossTask/blob/master/LICENSE) that are subject to their respective original licenses. Users must adhere to the terms and conditions specified by these licenses.
+This project does not impose any additional constraints beyond those stipulated in the original licenses. Users must ensure their usage complies with all applicable laws and regulations.
+This repository is released under the **MIT**. See [LICENSE](LICENSE) for details.
+
+### Citation Information
+
+If you find this work useful, please use the given bibtex entry to cite our work:
+
+```
+@misc{sarkar2025vcrbench,
+      title={VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models}, 
+      author={Pritam Sarkar and Ali Etemad},
+      year={2025},
+      eprint={2505.08455},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+}
+```
+
+
+### Contact
+For any queries please create an issue at [VCRBench](https://github.com/pritamqu/VCRBench).

data.parquet

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dataset.py

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+
+import torch
+import os
+import json
+
+PROMPT='''The given video consists of multiple short clips, each showing a different segment needed to complete the task: {goal}. 
+These clips are randomly shuffled, and your job is to arrange them in the correct order to complete the task: {goal}. 
+The clip numbers are mentioned at the beginning of each clip as Clip 1, Clip 2, and so on.
+In order to solve this task, first, you should identify the activity that is performed in each clip, and then use your reasoning and common sense to arrange these clips to successfully complete the task.
+
+The final output should be in this format:
+
+Correct order: <mention the Clip numbers separated by a comma>
+'''
+
+pp_prompts=dict(
+    default=PROMPT,
+    rrd="",
+)
+valid_modes=list(pp_prompts.keys())
+
+class VCRBench(torch.utils.data.Dataset):
+    """basic dataset for inference"""
+
+    def __init__(
+        self,
+        question_file,
+        video_root,
+        mode='default',
+        video_load_fn=None,
+        load_video_kwargs={}
+    ) -> None:
+        super(VCRBench, self).__init__()
+        self.data = json.load(open(question_file))
+        self.video_load_fn = video_load_fn
+        self.load_video_kwargs = load_video_kwargs
+        assert mode in valid_modes, print(f"choose from: {valid_modes}")
+        self.mode = mode
+        self.prompt = pp_prompts[mode]
+        self.video_root = os.path.join(video_root, 'videos')
+
+    def __len__(self) -> int:
+        return len(self.data)
+    
+    def prepare_question(self, sample):
+        if self.mode in ['default']:
+            prompt=self.prompt.format(goal=sample['goal'])
+        elif self.mode in ['rrd']:
+            prompt=sample['goal']
+        else:
+            raise ValueError()
+        return prompt
+    
+    def prepare_answer(self, sample):
+        gt = [o+1 for o in sample['ground_truth']]
+        return gt
+
+    def prepare_sample(self, sample):
+        video_file = os.path.join(self.video_root, sample['video_file'])
+        if self.video_load_fn:
+            video = self.video_load_fn(video_file, **self.load_video_kwargs)
+        else:
+            video = video_file
+
+        return {'video': video, 
+                'video_file': sample['video_file'],
+                'qid': sample['qid'],
+                'question': self.prepare_question(sample), 
+                'answer': self.prepare_answer(sample)}
+
+    def __getitem__(self, i):
+        sample = self.data[i]
+        return self.prepare_sample(sample)
+    
+
+if __name__=='__main__':
+
+    dataset=VCRBench(question_file="./data.json", 
+                    video_root="./",
+                    mode='default', 
+                    )
+        
+    for sample in dataset:
+        print(f"question: {sample['question']}")
+        print(f"video file: {sample['video_file']}")
+        print(f"answer: {sample['answer']}")
+        print('*'*10)
+
+        break
+
+        

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