Add dataset card with task description, fields, and baseline results

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- audio-classification
+language:
+- en
+tags:
+- music
+- piano
+- midi
+- competition
+- ranking
+- chopin
+pretty_name: Chopin Piano Competition 2015 – Preliminary Round MIDI
+size_categories:
+- n<1K
+---
+
+# Chopin Piano Competition 2015 – Preliminary Round MIDI
+
+Piano MIDI transcriptions of all 130 performers from the **preliminary round** of the
+[18th International Chopin Piano Competition (Warsaw, 2015)](https://chopincompetition.pl/en/),
+with competition labels for pairwise ranking evaluation.
+
+Companion dataset for the [EVPMR benchmark](https://github.com/anusfoil/eval-piano-midi-repr)
+(Evaluation of Piano MIDI Representations).
+
+## Dataset Description
+
+Each performer submitted a ~30-minute recital. Audio was sourced from the official
+Chopin Institute YouTube channel and transcribed to MIDI using a piano transcription model.
+
+**Task**: Given two performances, predict which performer advanced further in the competition.
+This is a **pairwise ranking** problem (chance accuracy = 0.50).
+
+## Data
+
+```
+data/{performer_slug}/{title}.mid     — 130 MIDI files (one per performer)
+metadata/splits.csv                   — train/val/test split assignments
+metadata/metadata_raw.csv            — per-performer metadata
+```
+
+### `metadata/splits.csv`
+
+| Column | Description |
+|--------|-------------|
+| `midi_path` | Relative path to MIDI file within `data/` |
+| `label` | Competition round score (0–4); higher = advanced further |
+| `split` | `train` / `val` / `test` |
+
+Split sizes: **90 train / 20 val / 20 test** (stratified by round score, seed=42).
+
+### `metadata/metadata_raw.csv`
+
+| Column | Description |
+|--------|-------------|
+| `performer_name` | Performer full name |
+| `country` | Country code |
+| `round_score` | Competition label (0–4) |
+| `passed_prelim` | 1 if advanced past preliminary round, else 0 |
+| `video_id` | YouTube video ID (source audio) |
+| `video_title` | YouTube video title |
+| `video_url` | Full YouTube URL |
+| `match_score` | Audio-to-MIDI alignment confidence score |
+
+### Round score mapping
+
+| Score | Meaning |
+|-------|---------|
+| 0 | Did not pass preliminary round |
+| 1–4 | Passed preliminary; higher = advanced further in the competition |
+
+## Usage
+
+```python
+from evpmr import ChopinCompTask
+
+task = ChopinCompTask()
+split = task.load_split(hf_download=True)  # auto-downloads this dataset
+```
+
+Or download manually:
+
+```python
+from huggingface_hub import snapshot_download
+path = snapshot_download("anusfoil/chopin-comp-midi", repo_type="dataset")
+# MIDI files at: path/data/{performer_slug}/*.mid
+# Splits at:     path/metadata/splits.csv
+```
+
+## Baseline Results (EVPMR)
+
+Evaluated with [Aria-medium](https://huggingface.co/loubb/aria-medium-embedding)
+(frozen encoder, windowed encoding ~10–15 s per window):
+
+| Probe | Pairwise Accuracy | Antisymmetry |
+|-------|:-----------------:|:------------:|
+| Linear (binary LogReg on concat embeddings) | 0.562 | 1.000 |
+| Attentive (cross-attention, I-JEPA style) | 0.689 | 0.992 |
+
+Chance level = 0.50. Antisymmetry measures consistency: a perfect ranker always
+reverses its prediction when the pair is swapped (score = 1.0).
+
+## Provenance & License
+
+- Audio source: [Chopin Institute YouTube channel](https://www.youtube.com/@ChopinInstitute) (CC BY)
+- MIDI transcription: automated piano transcription
+- Competition results: publicly available from [chopincompetition.pl](https://chopincompetition.pl)
+
+This dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+Please cite the competition when using this data.