Add metadata and improve model card

Hi! I'm Niels, part of the community science team at Hugging Face.

I noticed this model repository was missing some structured metadata and could benefit from improved documentation. This PR adds relevant YAML metadata (license, pipeline tag, and dataset link) to the model card and formats the content to highlight the research paper and official code repository. This helps make the model more discoverable and easier for other researchers to use.

README.md

@@ -1,58 +1,34 @@
-# Skip-BART
-
-The description is generated by Grok3.
-
-## Model Details
-
-- **Model Name**: Skip-BART
-
-- **Model Type**: Transformer-based model (BART architecture) for automatic stage lighting control
-
-- **Version**: 1.0
-
-- **Release Date**: August 2025
-
-- **Developers**: Zijian Zhao, Dian Jin
-
-- **Organization**: HKUST, PolyU
-
-- **License**: Apache License 2.0
-
-- **Paper**: [Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://arxiv.org/abs/2506.01482)
+---
+license: apache-2.0
+pipeline_tag: other
+datasets:
+- RS2002/RPMC-L2
+tags:
+- stage-lighting
+- generative-task
+- music-to-light
+---
 
-- **Citation:**
-
-  ```
-  @article{zhao2025automatic,
-    title={Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?},
-    author={Zhao, Zijian and Jin, Dian and Zhou, Zijing and Zhang, Xiaoyu},
-    journal={arXiv preprint arXiv:2506.01482},
-    year={2025}
-  }
-  ```
+# Skip-BART
 
-- **Contact**: zzhaock@connect.ust.hk
+Skip-BART is an end-to-end generative model designed for **Automatic Stage Lighting Control (ASLC)**. Unlike traditional rule-based methods, Skip-BART conceptualizes lighting control as a generative task, learning directly from professional lighting engineers to predict vivid, human-like lighting sequences synchronized with music.
 
-- **Repository**: https://github.com/RS2002/Skip-BART
+This model was presented in the paper [Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?](https://huggingface.co/papers/2506.01482).
 
-## Model Description
+- **Repository**: [https://github.com/RS2002/Skip-BART](https://github.com/RS2002/Skip-BART)
+- **Dataset**: [RS2002/RPMC-L2](https://huggingface.co/datasets/RS2002/RPMC-L2)
 
-Skip-BART is a transformer-based model built on the Bidirectional and Auto-Regressive Transformers (BART) architecture, designed for automatic stage lighting control. It generates lighting sequences synchronized with music input, treating stage lighting as a generative task. The model processes music data in an octuple format and outputs lighting control parameters, leveraging a skip-connection-enhanced BART structure for improved performance.
+## Model Details
 
-- **Architecture**: BART with skip connections
-- **Input Format**: Encoder input (batch_size, length, 512), decoder input (batch_size, length, 2), attention masks (batch_size, length)
-- **Output Format**: Hidden states of dimension [batch_size, length, 1024]
-- **Hidden Size**: 1024
-- **Training Objective**: Pre-training on music data, followed by fine-tuning for lighting sequence generation
-- **Tasks Supported**: Stage lighting sequence generation
+- **Model Type**: Transformer-based model (BART architecture) with skip connections.
+- **Task**: Stage lighting sequence generation (predicting light hue and intensity).
+- **Architecture**: BART-based structure enhanced with a novel skip-connection mechanism to strengthen the relationship between musical frames and lighting states.
+- **Input Format**: Encoder input (batch_size, length, 512) for audio features; Decoder input (batch_size, length, 2) for lighting parameters.
+- **Output Format**: Hidden states representing lighting control parameters (dimension 1024).
 
 ## Training Data
 
-The model was trained on the **RPMC-L2** dataset:
-
-- **Dataset Source**: [RPMC-L2](https://zenodo.org/records/14854217?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjM5MDcwY2E5LTY0MzUtNGZhZC04NzA4LTczMjNhNTZiOGZmYSIsImRhdGEiOnt9LCJyYW5kb20iOiI1YWRkZmNiMmYyOGNiYzI4ZWUxY2QwNTAyY2YxNTY4ZiJ9.0Jr6GYfyyn02F96eVpkjOtcE-MM1wt-_ctOshdNGMUyUKI15-9Rfp9VF30_hYOTqv_9lLj-7Wj0qGyR3p9cA5w)
-- **Description**: Contains music and corresponding stage lighting data in a format suitable for training Skip-BART.
-- **Details**: Refer to the [paper](https://arxiv.org/abs/2506.01482) for dataset specifics.
+The model was trained on the **RPMC-L2** dataset, a self-collected dataset containing music and corresponding stage lighting data synchronized within a frame grid.
 
 ## Usage
 
@@ -64,6 +40,8 @@ git clone https://huggingface.co/RS2002/Skip-BART
 
 ### Example Code
 
+The following snippet demonstrates how to load the model and perform a forward pass (requires `model.py` from the official repository).
+
 ```python
 import torch
 from model import Skip_BART
@@ -80,4 +58,19 @@ decoder_attention_mask = torch.zeros((2, 1024))
 # Forward pass
 output = model(x_encoder, x_decoder, encoder_attention_mask, decoder_attention_mask)
 print(output.size())  # Output: [2, 1024, 1024]
-```
+```
+
+## Citation
+
+```bibtex
+@article{zhao2025automatic,
+  title={Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task?},
+  author={Zhao, Zijian and Jin, Dian and Zhou, Zijing and Zhang, Xiaoyu},
+  journal={arXiv preprint arXiv:2506.01482},
+  year={2025}
+}
+```
+
+## Contact
+
+Zijian Zhao: zzhaock@connect.ust.hk