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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]
+# Adversarial-MidiBERT
+
+The description is generated by Grok3.
+
+
+
+## Model Details
+
+- **Model Name**: Adversarial-MidiBERT
+
+- **Model Type**: Transformer-based model for symbolic music understanding
+
+- **Version**: 1.0
+
+- **Release Date**: August 2025
+
+- **Developers**: Zijian Zhao
+
+- **Organization**: SYSU
+
+- **License**: Apache License 2.0
+
+- **Paper**: [Let Network Decide What to Learn: Symbolic Music Understanding Model Based on Large-scale Adversarial Pre-training](https://dl.acm.org/doi/abs/10.1145/3731715.3733483), ACM ICMR 2025
+
+- Citation:
+
+  ```
+  @inproceedings{zhao2025let,
+    title={Let Network Decide What to Learn: Symbolic Music Understanding Model Based on Large-scale Adversarial Pre-training},
+    author={Zhao, Zijian},
+    booktitle={Proceedings of the 2025 International Conference on Multimedia Retrieval},
+    pages={2128--2132},
+    year={2025}
+  }
+  ```
+
+- **Contact**: zhaozj28@mail2.sysu.edu.cn
+
+- **Repository**: https://github.com/RS2002/Adversarial-MidiBERT
+
+
+
+## Model Description
+
+Adversarial-MidiBERT is a transformer-based model designed for symbolic music understanding, leveraging large-scale adversarial pre-training. It builds upon the [MidiBERT-Piano](https://github.com/wazenmai/MIDI-BERT) framework and extends it with adversarial pre-training techniques to enhance performance on music-related tasks. The model processes symbolic music data in an octuple format and can be fine-tuned for various downstream tasks such as music generation, classification, and analysis.
+
+- **Architecture**: Transformer-based (based on MidiBERT)
+- **Input Format**: Octuple representation of symbolic music (batch_size, sequence_length, 8)
+- **Output Format**: Hidden states of dimension [batch_size, sequence_length, 768]
+- **Hidden Size**: 768
+- **Training Objective**: Adversarial pre-training followed by task-specific fine-tuning
+- **Tasks Supported**: Symbolic music understanding tasks
+
+## Training Data
+
+The model was pre-trained and fine-tuned on the following datasets:
+
+- **POP1K7**: A dataset of popular music MIDI files.
+- **POP909**: A dataset of 909 pop songs in MIDI format.
+- **Pinaist8**: A dataset of piano performances.
+- **EMOPIA**: A dataset for emotion-based music analysis.
+- **GiantMIDI**: A large-scale MIDI dataset.
+
+For details on dataset preprocessing and dictionary files, refer to the [PianoBART repository](https://github.com/RS2002/PianoBart). Pre-training data should be placed in `./Data/output_pretrain`.
+
+
+
+## Usage
+
+### Installation
+
+```shell
+git clone https://huggingface.co/RS2002/Adversarial-MidiBERT
+```
+
+Please ensure that the `model.py` and `Octuple.pkl` files are located in the same folder.
+
+### Example Code
+
+```python
+import torch
+from model import Adversarial_MidiBERT
+
+# Load the model
+model = Adversarial_MidiBERT.from_pretrained("RS2002/Adversarial-MidiBERT")
+
+# Example input
+input_ids = torch.randint(0, 10, (2, 1024, 8))
+attention_mask = torch.zeros((2, 1024))
+
+# Forward pass
+y = model(input_ids, attention_mask)
+print(y.last_hidden_state.shape)  # Output: [2, 1024, 768]
+```