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

This PR improves the model card by adding relevant metadata at the top using YAML:
- `license: mit` as specified in the repository.
- `pipeline_tag: image-feature-extraction` to ensure discoverability for models that extract features from images.
- `library_name: transformers` as the model is compatible with the Hugging Face Transformers library, allowing for better Hub integration.

This enhances the model's visibility and usability on the Hugging Face Hub.

README.md

@@ -1,3 +1,9 @@
+---
+license: mit
+pipeline_tag: image-feature-extraction
+library_name: transformers
+---
+
 # Model Card for HP (High-Preference) Model
 
 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
 
 ### Key Features
 
-- **Image-Only Evaluation**: No text input required, focuses purely on visual quality
-- **Human Preference Aligned**: Trained on preference triplets from [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset
-- **Complementary Design**: Works optimally when combined with [ICT model](https://huggingface.co/8y/ICT) for comprehensive evaluation
+-   **Image-Only Evaluation**: No text input required, focuses purely on visual quality
+-   **Human Preference Aligned**: Trained on preference triplets from [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset
+-   **Complementary Design**: Works optimally when combined with [ICT model](https://huggingface.co/8y/ICT) for comprehensive evaluation
 
 ### Model Sources
 
-* **Repository:** [https://github.com/BarretBa/ICTHP](https://github.com/BarretBa/ICTHP)
-* **Paper:** [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://arxiv.org/abs/2507.19002)
-* **Base Model:** CLIP-ViT-H-14 (Image Encoder + MLP Head)
-* **Training Dataset:** [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset (360,000 preference triplets)
+*   **Repository:** [https://github.com/BarretBa/ICTHP](https://github.com/BarretBa/ICTHP)
+*   **Paper:** [Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment](https://arxiv.org/abs/2507.19002)
+*   **Base Model:** CLIP-ViT-H-14 (Image Encoder + MLP Head)
+*   **Training Dataset:** [Pick-High datase](https://huggingface.co/datasets/8y/Pick-High-Dataset) and Pick-a-pic dataset (360,000 preference triplets)
 
 ## How to Get Started with the Model
 
@@ -102,4 +108,4 @@ This model was trained on 36000 preference triplets from [Pick-High datase](http
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2507.19002}, 
 }
-```
+```