Update README.md

README.md

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 MSVC is a set of collected video captioning data. It is constructed to ensure a robust and thorough evaluation of Video-LLMs' video-captioning capabilities.
 
 **Dataset detail:**
-MSVC is introduced to address limitations in existing video caption benchmarks, MSVC samples 500 videos with human-annotated captions from [MSVD](https://www.aclweb.org/anthology/P11-1020/), [MSRVTT](http://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf), and [VATEX](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_VaTeX_A_Large-Scale_High-Quality_Multilingual_Dataset_for_Video-and-Language_Research_ICCV_2019_paper.pdf), ensuring diverse scenarios and domains. 
+MSVC is introduced to address limitations in existing video caption benchmarks, MSVC samples a total of 1,500 videos with human-annotated captions from [MSVD](https://www.aclweb.org/anthology/P11-1020/), [MSRVTT](http://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf), and [VATEX](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_VaTeX_A_Large-Scale_High-Quality_Multilingual_Dataset_for_Video-and-Language_Research_ICCV_2019_paper.pdf), ensuring diverse scenarios and domains. 
 Traditional evaluation metrics rely on exact match statistics between generated and ground truth captions, which are limited in capturing the richness of video content. Thus, we use a ChatGPT-assisted evaluation similar to [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT/blob/main/quantitative_evaluation/README.md). Both generated and human-annotated captions are evaluated by GPT-3.5-turbo (0613) for Correctness of Information and Detailed Orientation.
 It is worth noting that each video in the MSVC benchmark is annotated with multiple human-written captions, covering different aspects of the video. This comprehensive annotation ensures a robust and thorough evaluation of Video-LLMs.