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---
license: apache-2.0
task_categories:
- video-text-to-text
---
## V-NIAH-D Benchmark
A Visual Needle-In-A-Haystack Benchmark with Periodic Distractors. It was presented in [VideoRoPE: What Makes for Good Video Rotary Position Embedding?](https://huggingface.co/papers/2502.05173).
One can use it by following steps similar to [V-NIAH](https://github.com/EvolvingLMMs-Lab/LongVA).
## VideoRoPE Training Data
To facilitate the reproduction of our experimental results, we have also uploaded the data used by VideoRoPE. We use a subset of the [LLaVA-Video-178K dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) to train VideoRoPE.
The LLaVA-Video-178K dataset consists of 178K videos and approximately 5 million question-answer (QA) pairs from diverse sources such as HD-VILA, Kinetics, and ActivityNet. To balance training efficiency and long-video comprehension, we randomly select 136K videos with durations under 2 minutes and 18K videos with durations between 2 and 3 minutes. This process resulted in our training set containing approximately 1.3 million pairs.
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