Create README.md

README.md

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+---
+library_name: symupe
+license: cc-by-nc-sa-4.0
+datasets:
+- loubb/aria-midi
+pipeline_tag: feature-extraction
+tags:
+- music
+- piano
+- midi
+- pre-training
+- transformer
+- MLM
+---
+
+# SyMuPe: Aria-MIDI MLM Backbone
+
+**Aria-MIDI-MLM** is a 12-layer Transformer encoder designed for symbolic piano music feature extraction. 
+It was pre-trained using a **Multi-Mask Language Modeling (mMLM)** objective on 371,053 diverse piano MIDI files from the deduped subset of [Aria-MIDI](https://huggingface.co/datasets/loubb/aria-midi) dataset.
+
+This model serves as the foundation for the [MIDI Quality Classifier](https://huggingface.co/SyMuPe/MIDI-Quality-Classifier), presented in the article: [**PianoCoRe: Combined and Refined Piano MIDI Dataset**](https://doi.org/10.5334/tismir.333).
+
+- **SyMuPe Repo:** https://github.com/ilya16/SyMuPe
+- **Project Repo:** https://github.com/ilya16/PianoCoRe
+- **Dataset:** https://huggingface.co/datasets/loubb/aria-midi
+
+## Architecture
+
+- **Type:** Transformer Encoder
+- **Configuration:** 12 layers, 768 hidden dimensions, 12 attention heads.
+- **Objective:** Multi-Mask Language Modeling (mMLM).
+- **Inputs (score-agnostic):** `Pitch`, `Velocity`, `TimeShift`, `Duration`, absolute `TimePosition`
+- **Training:** Pre-trained for 600,000 steps on 512-note sequences sampled from the deduplicated [Aria-MIDI](https://huggingface.co/datasets/loubb/aria-midi) corpus.
+
+## Quick Start
+
+Before using this model, ensure you have the `symupe` library installed (`pip install -U symupe`).
+
+```python
+import torch
+from symupe import AutoEmbedder
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Build Embedder by loading the model and tokenizer directly from the Hub
+embedder = AutoEmbedder.from_pretrained("SyMuPe/Aria-MIDI-MLM", device=device)
+# model, tokenizer = embedder.model, embedder.tokenizer
+
+# Extract embeddings from a MIDI file
+result = embedder("performance.mid", max_seq_len=512, hop_size=256, layer=-1)
+# result is MusicEmbeddingResult(...) containing:
+# - midi, seq, embeddings, memory_tokens, token_embeddings, hidden_states, sequences and window_indices
+
+print(result.embeddings.shape)  # (windows, seq_len, emb_dim)
+```
+
+## License
+
+The model weights are distributed under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) license.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@inproceedings{borovik2025symupe,
+  title = {{SyMuPe: Affective and Controllable Symbolic Music Performance}},
+  author = {Borovik, Ilya and Gavrilev, Dmitrii and Viro, Vladimir},
+  year = {2025},
+  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
+  pages = {10699--10708},
+  doi = {10.1145/3746027.3755871}
+}
+```
+
+```bibtex
+@article{borovik2026pianocore,
+  title = {{PianoCoRe: Combined and Refined Piano MIDI Dataset}},
+  author = {Borovik, Ilya},
+  year = {2026},
+  journal = {Transactions of the International Society for Music Information Retrieval},
+  volume = {9},
+  number = {1},
+  pages = {144--163},
+  doi = {10.5334/tismir.333}
+}
+```