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README.md

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-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
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-
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# 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)
+
+- **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}
+  }
+  ```
+
+- **Contact**: zzhaock@connect.ust.hk
+
+- **Repository**: https://github.com/RS2002/Skip-BART
+
+## Model Description
+
+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.
+
+- **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
+
+## 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.
+
+## Usage
+
+### Installation
+
+```shell
+git clone https://huggingface.co/RS2002/Skip-BART
+```
+
+### Example Code
+
+```python
+import torch
+from model import Skip_BART
+
+# Load the model
+model = Skip_BART.from_pretrained("RS2002/Skip-BART")
+
+# Example input
+x_encoder = torch.rand((2, 1024, 512))
+x_decoder = torch.randint(0, 10, (2, 1024, 2))
+encoder_attention_mask = torch.zeros((2, 1024))
+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]
+```