Datasets:

Rapidata
/

text-2-video-human-preferences-sora-2

---
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: video1
    dtype: string
  - name: video2
    dtype: string
  - name: weighted_results1_Alignment
    dtype: float64
  - name: weighted_results2_Alignment
    dtype: float64
  - name: detailedResults_Alignment
    list:
    - name: userDetails
      struct:
      - name: age
        dtype: string
      - name: country
        dtype: string
      - name: gender
        dtype: string
      - name: language
        dtype: string
      - name: occupation
        dtype: string
      - name: userScores
        struct:
        - name: global
          dtype: float64
    - name: votedFor
      dtype: string
  - name: weighted_results1_Coherence
    dtype: float64
  - name: weighted_results2_Coherence
    dtype: float64
  - name: detailedResults_Coherence
    list:
    - name: userDetails
      struct:
      - name: age
        dtype: string
      - name: country
        dtype: string
      - name: gender
        dtype: string
      - name: language
        dtype: string
      - name: occupation
        dtype: string
      - name: userScores
        struct:
        - name: global
          dtype: float64
    - name: votedFor
      dtype: string
  - name: weighted_results1_Preference
    dtype: float64
  - name: weighted_results2_Preference
    dtype: float64
  - name: detailedResults_Preference
    list:
    - name: userDetails
      struct:
      - name: age
        dtype: string
      - name: country
        dtype: string
      - name: gender
        dtype: string
      - name: language
        dtype: string
      - name: occupation
        dtype: string
      - name: userScores
        struct:
        - name: global
          dtype: float64
    - name: votedFor
      dtype: string
  - name: file_name1
    dtype: string
  - name: file_name2
    dtype: string
  - name: model1
    dtype: string
  - name: model2
    dtype: string
  splits:
  - name: train
    num_bytes: 6309545
    num_examples: 1389
  download_size: 645406
  dataset_size: 6309545
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- video-classification
- text-to-video
- text-classification
language:
- en
tags:
- videos
- t2v
- text-2-video
- text2video
- text-to-video
- human
- annotations
- preferences
- likert
- coherence
- alignment
- wan
- wan 2.1
- veo2
- veo
- pikka
- alpha
- sora
- hunyuan
- veo3
- mochi-1
- seedance-1-pro
- seedance
- seedance 1
- Marey
- moonvalley
- sora2
- openai
pretty_name: Sora 2 Human Preferences
size_categories:
- 1K<n<10K
---

<style>

.vertical-container {
    display: flex;  
    flex-direction: column;
    gap: 60px;  
}

.image-container img {
  height: 150px; /* Set the desired height */
  margin:0;
  object-fit: contain; /* Ensures the aspect ratio is maintained */
  width: auto; /* Adjust width automatically based on height */
}

.image-container {
  display: flex; /* Aligns images side by side */
  justify-content: space-around; /* Space them evenly */
  align-items: center; /* Align them vertically */
}

  .container {
    width: 90%;
    margin: 0 auto;
  }

  .text-center {
    text-align: center;
  }

  .score-amount {
margin: 0;
margin-top: 10px;
  }

  .score-percentage {
    font-size: 12px;
    font-weight: semi-bold;
  }
  
</style>

# Rapidata Video Generation Sora 2 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, ~75k human responses from ~15k human annotators were collected to evaluate the Sora 2 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, ~75k human responses from ~15k human annotators were collected to evaluate the Sora 2 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-moonvalley-marey/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>A futuristic architect showcases a holographic city blueprint, swiping through towering skyscrapers and intricate roadways with smooth gestures, merging digital design with tangible reality.</q>
      </div>
    <div class="image-container">
      <div>
        <h3 class="score-amount">Marey </h3>
        <div class="score-percentage">(Score: 88.72%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/marey-11-8-25_99_0.gif" width=500>
      </div>
      <div>
        <h3 class="score-amount">Hunyuan </h3>
        <div class="score-percentage">(Score: 11.28%)</div>
        <img src="https://assets.rapidata.ai/hunyuan_0099_1724.gif" width=500>
      </div>
    </div>
  </div>
  <div class="container">
    <div class="text-center">
      <q>A firefighter in action battles flames, the camera alternating between his determined face and the roaring blaze as he rescues those in danger.</q>
    </div>
    <div class="image-container">
      <div>
        <h3 class="score-amount">Marey </h3>
        <div class="score-percentage">(Score: 4.06%)</div>
        <img src="https://assets.rapidata.ai/marey-11-8-25_98_0.gif" width=500>
      </div>
      <div>
        <h3 class="score-amount">Wan2.1 </h3>
        <div class="score-percentage">(Score: 95.94%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/wan2.1_0098_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">Marey </h3>
         <div class="score-percentage">(Glitch Rating: 0%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/marey-11-8-25_82_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
         <h3 class="score-amount">Pika2.2 </h3>
         <div class="score-percentage">(Glitch Rating: 100%)</div>
         <img src="https://assets.rapidata.ai/pika2.2_0082_0.gif" width="500" alt="Dataset visualization">
      </div>
    </div>
  </div>
  <div class="container">
    <div class="image-container">
      <div>
        <h3 class="score-amount">Marey </h3>
        <div class="score-percentage">(Glitch Rating: 89.36%)</div>
        <img src="https://assets.rapidata.ai/marey-11-8-25_8_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
        <h3 class="score-amount">Ray 2 </h3>
        <div class="score-percentage">(Glitch Rating: 10.64%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/ray2_0008_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">Marey </h3>
        <div class="score-percentage">(Score: 89.13%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/marey-11-8-25_43_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
        <h3 class="score-amount">Hunyuan </h3>
        <div class="score-percentage">(Score: 10.87%)</div>
        <img src="https://assets.rapidata.ai/hunyuan_0043_421.gif" width="500" alt="Dataset visualization">
      </div>
    </div>
  </div>
  <div class="container">
    <div class="image-container">
      <div>
         <h3 class="score-amount">Marey </h3>
         <div class="score-percentage">(Score: 3.33%)</div>
         <img src="https://assets.rapidata.ai/marey-11-8-25_36_0.gif" width="500" alt="Dataset visualization">
      </div>
      <div>
         <h3 class="score-amount">Veo 3 </h3>
         <div class="score-percentage">(Score: 96.67%)</div>
        <img style="border: 5px solid #18c54f;" src="https://assets.rapidata.ai/veo3_0036_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)