---
title: text-2-video-human-preferences-kling-v2.1-master
canonical_url: "https://www.modelscope.cn/datasets/Rapidata/text-2-video-human-preferences-kling-v2.1-master"
md_url: "https://www.modelscope.cn/datasets/Rapidata/text-2-video-human-preferences-kling-v2.1-master.md"
repository: Rapidata/text-2-video-human-preferences-kling-v2.1-master
last_updated: 2025-08-01
license: "Apache License 2.0"
storage_size: "657 KB"
downloads: 222
stars: 0
---

# text-2-video-human-preferences-kling-v2.1-master

> text-2-video-human-preferences-kling-v2.1-master - Rapidata 在 ModelScope 开源的数据集。.vertical-container { display: flex; flex-direction: column; gap: 60px; }

Rapidata/text-2-video-human-preferences-kling-v2.1-master 是 ModelScope 魔搭社区上的数据集，存储大小 657 KB，采用 Apache License 2.0 许可。

- **Repository**: Rapidata/text-2-video-human-preferences-kling-v2.1-master
- **License**: Apache License 2.0
- **Storage size**: 657 KB
- **Downloads**: 222
- **Stars**: 0
- **Last updated**: 2025-08-01

Source: https://www.modelscope.cn/datasets/Rapidata/text-2-video-human-preferences-kling-v2.1-master

---

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# Rapidata Video Generation Kling v2.1 Master Human Preference

<a href="https://www.rapidata.ai">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="300" alt="Dataset visualization">
</a>

<a href="https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback">
</a>

In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Kling v2.1 Master video generation model on our benchmark. This dataset was collected in roughtly 30 min using the [Rapidata Python API](https://docs.rapidata.ai), accessible to anyone and ideal for large scale data annotation.

Explore our latest model rankings on our [website](https://www.rapidata.ai/benchmark).

If you get value from this dataset and would like to see more in the future, please consider liking it ❤️

# Overview

In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Kling v2.1 Master video generation model on our benchmark. This dataset was collected in roughtly 30 min using the [Rapidata Python API](https://docs.rapidata.ai), accessible to anyone and ideal for large scale data annotation.
The benchmark data is accessible on [huggingface](https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences) directly.

# Explanation of the colums

The dataset contains paired video comparisons. Each entry includes 'video1' and 'video2' fields, which contain links to downscaled GIFs for easy viewing. The full-resolution videos can be found [here](https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-kling-v2.1-master/tree/main/Videos)

The weighted_results column contains scores ranging from 0 to 1, representing aggregated user responses. Individual user responses can be found in the detailedResults column.

# Alignment

The alignment score quantifies how well an video matches its prompt. Users were asked: "Which video fits the description better?".

## Examples

<div class="vertical-container">
  <div class="container">
      <div class="text-center">
         <q>Aerial view of synchronized swimmers performing intricate patterns in a crystal-clear lake, their movements fluid and graceful under the soft glow of the morning sun.</q>
      </div>
    <div class="image-container">
      <div>
        <h3 class="score-amount">Kling v2.1 Master </h3>
        <div class="score-percentage">(Score: 86.08%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/kling-v2.1-master-25-7-25_91_0.gif" width=500>
      </div>
      <div>
        <h3 class="score-amount">Hunyuan </h3>
        <div class="score-percentage">(Score: 13.92%)</div>
        <img src="https://assets.rapidata.ai/hunyuan_0091_421.gif" width=500>
      </div>
    </div>
  </div>
  <div class="container">
    <div class="text-center">
      <q>A hyper-realistic view of an astronaut inside a spaceship, gazing out at Earth through a large window. Soft ambient light highlights the control panels, creating a serene yet awe-inspiring atmosphere.</q>
    </div>
    <div class="image-container">
      <div>
        <h3 class="score-amount">Kling v2.1 Master </h3>
        <div class="score-percentage">(Score: 11.26%)</div>
        <img src="https://assets.rapidata.ai/kling-v2.1-master-25-7-25_59_0.gif" width=500>
      </div>
      <div>
        <h3 class="score-amount">Sora </h3>
        <div class="score-percentage">(Score: 88.74%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/sora_0059_0.gif" width=500>
      </div>
    </div>
  </div>
</div>


# Coherence

The coherence score measures whether the generated video is logically consistent and free from artifacts or visual glitches. Without seeing the original prompt, users were asked: "Which video has more glitches and is more likely to be AI generated?"

## Examples

<div class="vertical-container">
  <div class="container">
    <div class="image-container">
      <div>
         <h3 class="score-amount">Kling v2.1 Master </h3>
         <div class="score-percentage">(Glitch Rating: 7.35%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/kling-v2.1-master-25-7-25_36_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
         <h3 class="score-amount">Sora </h3>
         <div class="score-percentage">(Glitch Rating: 92.75%)</div>
         <img src="https://assets.rapidata.ai/sora_0036_0.gif" width="500" alt="Dataset visualization">
      </div>
    </div>
  </div>
  <div class="container">
    <div class="image-container">
      <div>
        <h3 class="score-amount">Kling v2.1 Master </h3>
        <div class="score-percentage">(Glitch Rating: 87.97%)</div>
        <img src="https://assets.rapidata.ai/kling-v2.1-master-25-7-25_37_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
        <h3 class="score-amount">Ray 2 </h3>
        <div class="score-percentage">(Glitch Rating: 12.03%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/ray2_0037_2.gif" width="500" alt="Dataset visualization">
      </div>
    </div>      
  </div>
</div>

# Preference

The preference score reflects how visually appealing participants found each video, independent of the prompt. Users were asked: "Which video do you prefer aesthetically?"

## Examples

<div class="vertical-container">
  <div class="container">
    <div class="image-container">
      <div>
        <h3 class="score-amount">Kling v2.1 Master </h3>
        <div class="score-percentage">(Score: 94.77%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/kling-v2.1-master-25-7-25_11_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
        <h3 class="score-amount">Pika </h3>
        <div class="score-percentage">(Score: 5.23%)</div>
        <img src="https://assets.rapidata.ai/pika_0011_2286430682.gif" width="500" alt="Dataset visualization">
      </div>
    </div>
  </div>
  <div class="container">
    <div class="image-container">
      <div>
         <h3 class="score-amount">Kling v2.1 Master </h3>
         <div class="score-percentage">(Score: 14.66%)</div>
         <img src="https://assets.rapidata.ai/kling-v2.1-master-25-7-25_64_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
         <h3 class="score-amount">Seedance 1 Pro </h3>
         <div class="score-percentage">(Score: 85.34%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/seedance-1-pro-24-7-25_64_0.gif " width="500" alt="Dataset visualization">
      </div>
    </div>      
  </div>
</div>

</br>

# About Rapidata

Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai/) to learn more about how we're revolutionizing human feedback collection for AI development.

# Other Datasets

We run a benchmark of the major video generation models, the results can be found on our [website](https://www.rapidata.ai/leaderboard/video-models). We rank the models according to their coherence/plausiblity, their aligment with the given prompt and style prefernce. The underlying 2M+ annotations can be found here:

- Link to the [Rich Video Annotation dataset](https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback)
- Link to the [Coherence dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset)
- Link to the [Text-2-Image Alignment dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset)
- Link to the [Preference dataset](https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3)