Add metadata (license, pipeline tag, library name) to model card

#1
by nielsr HF Staff - opened
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  1. README.md +14 -8
README.md CHANGED
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  # Model Card for HP (High-Preference) Model
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  This model is a specialized human preference scoring function that evaluates image quality based purely on visual aesthetics and human preferences, without relying on text-image alignment. See our paper [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://arxiv.org/abs/2507.19002) for more details.
@@ -12,16 +18,16 @@ The HP (High-Preference) model represents a paradigm shift in image quality eval
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  ### Key Features
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- - **Image-Only Evaluation**: No text input required, focuses purely on visual quality
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- - **Human Preference Aligned**: Trained on preference triplets from [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset
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- - **Complementary Design**: Works optimally when combined with [ICT model](https://huggingface.co/8y/ICT) for comprehensive evaluation
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  ### Model Sources
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- * **Repository:** [https://github.com/BarretBa/ICTHP](https://github.com/BarretBa/ICTHP)
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- * **Paper:** [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://arxiv.org/abs/2507.19002)
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- * **Base Model:** CLIP-ViT-H-14 (Image Encoder + MLP Head)
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- * **Training Dataset:** [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset (360,000 preference triplets)
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  ## How to Get Started with the Model
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  primaryClass={cs.CV},
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  url={https://arxiv.org/abs/2507.19002},
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  }
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- ```
 
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+ ---
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+ license: mit
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+ pipeline_tag: image-feature-extraction
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+ library_name: transformers
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+ ---
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+
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  # Model Card for HP (High-Preference) Model
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  This model is a specialized human preference scoring function that evaluates image quality based purely on visual aesthetics and human preferences, without relying on text-image alignment. See our paper [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://arxiv.org/abs/2507.19002) for more details.
 
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  ### Key Features
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+ - **Image-Only Evaluation**: No text input required, focuses purely on visual quality
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+ - **Human Preference Aligned**: Trained on preference triplets from [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset
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+ - **Complementary Design**: Works optimally when combined with [ICT model](https://huggingface.co/8y/ICT) for comprehensive evaluation
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  ### Model Sources
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+ * **Repository:** [https://github.com/BarretBa/ICTHP](https://github.com/BarretBa/ICTHP)
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+ * **Paper:** [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://arxiv.org/abs/2507.19002)
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+ * **Base Model:** CLIP-ViT-H-14 (Image Encoder + MLP Head)
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+ * **Training Dataset:** [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset (360,000 preference triplets)
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  ## How to Get Started with the Model
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  primaryClass={cs.CV},
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  url={https://arxiv.org/abs/2507.19002},
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  }
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+ ```