Refine task categories and tags

README.md

@@ -2,10 +2,11 @@
 language:
 - en
 license: mit
-pretty_name: VCRBench
+size_categories:
+- n<1K
 task_categories:
-- video-text-to-text
-- visual-question-answering
+- video-classification
+pretty_name: VCRBench
 tags:
 - video
 - multimodal
@@ -14,15 +15,11 @@ tags:
 - multi-step-reasoning
 - long-form-reasoning
 - large-video-language-model
-- large-multimodal-model
-- multimodal-large-language-model
-size_categories:
-- n<1K
 configs:
 - config_name: default
   data_files:
   - split: test
-    path: "data.json"
+    path: data.json
 ---
 
 # VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models
@@ -36,42 +33,37 @@ configs:
 
 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)**.
+This repository provides the official implementation of **[VCRBench](https://arxiv.org/abs/2505.08455)**.  VCRBench is a benchmark dataset for evaluating the causal reasoning capabilities of Large Video Language Models (LVLMs) in visually grounded, goal-driven scenarios. It consists of procedural videos with shuffled steps, requiring LVLMs to identify, reason about, and correctly sequence events to achieve a specific goal.
 
 ![](./sample.png)
 
 ## Usage
 
-Please check our GitHub repo for the details of usage: [VCRBench](https://github.com/pritamqu/VCRBench)
+For detailed usage instructions, please refer to the GitHub repository: [VCRBench](https://github.com/pritamqu/VCRBench)
 
-```
+A basic example:
+
+```python
 from dataset import VCRBench
-dataset=VCRBench(question_file="data.json", 
-                video_root="./",
-                mode='default', 
-                )
-    
+dataset = VCRBench(question_file="data.json", video_root="./", mode='default')
 for sample in dataset:
-    print(sample['question'], )
-    print(sample['answer'], )
+    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.
+This dataset incorporates samples from [CrossTask](https://github.com/DmZhukov/CrossTask/blob/master/LICENSE) and is subject to their respective original licenses. This repository is released under the **MIT License**. See [LICENSE](LICENSE) for details. Users must adhere to the terms and conditions specified by these licenses.
+
 
 ### Citation Information
 
 If you find this work useful, please use the given bibtex entry to cite our work:
 
-```
+```bibtex
 @misc{sarkar2025vcrbench,
       title={VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models}, 
       author={Pritam Sarkar and Ali Etemad},