---
pretty_name: MetaScore-SFT-100
license: other
license_name: per-piece-source-licenses
license_link: LICENSE.md
size_categories:
  - n<1K
tags:
  - music
  - symbolic-music
  - midi
  - piano
  - music-generation
  - mirex
configs:
  - config_name: default
    data_files:
      - split: test
        path: test.jsonl
---

# MetaScore-SFT-100

## Overview

**MetaScore-SFT-100** is a fixed, 100-piece benchmark for symbolic piano continuation, derived from [MetaScore](https://github.com/wx83/MetaScore_Official) and developed to evaluate [DAMN](https://github.com/naivecynics/MIREX2026-DAMN).

Each example pairs a **4-bar piano prompt** (with an optional pickup) with a **12-bar reference continuation**, following the MIREX 2026 Symbolic Music Generation format. This is an independently curated development benchmark, **not the official MIREX test set**.

## Dataset

The benchmark contains **100 complete examples across 15 genre labels**, with fixed prompts, references, and source assets.

| Resource | Contents |
| --- | --- |
| [`test.jsonl`](test.jsonl) | 100 entries with CID, genre, source, and prompt/reference note sequences |
| [`challenge_test/`](challenge_test) | Fixed prompt/reference JSON, companion MIDIs, and window manifest |
| `midi/`, `mscz/`, `mp3/` | Complete-piece MIDI, MuseScore scores, and existing source previews |
| [`label/`](label) | Genre annotations |
| `manifest.csv`, `summary.json` | Source selection metadata and statistics |
| [`attribution.json`](attribution.json) | Per-piece attribution and source license records |
| [`checksums.sha256`](checksums.sha256) | File integrity checksums |

Notes use integer `(start, pitch, duration)` attributes on a sixteenth-note grid. Steps `0-15` are reserved for an optional pickup, prompt starts are in `0-79`, and reference starts are in `80-271`. Durations are positive and notes end no later than step `272`.

Source permissions apply per piece; see [LICENSE.md](LICENSE.md). The existing source-license conflict is documented in [rights_conflicts.json](rights_conflicts.json).

## Construction

We selected piano scores from a genre-stratified sample of MetaScore using these criteria:

- A single piano part, 4/4 meter, at least 16 measures, and no lyrics.
- The upstream best-unique-arrangement filter.
- A user rating of at least 4.5 from at least three ratings.

Automatic filtering retained 106 candidates; manual listening removed six clear quality-control failures, leaving 100 pieces. Genre counts are not balanced.

Deterministic extraction selects the first complete, protocol-compatible 16-bar window: **99 pieces use the opening window, and one uses a later window**. Fixed-window MIDIs use 480 ticks per quarter note, 120 BPM, and a fixed piano program. Complete-piece source assets retain their original contents; their MP3s are not renderings of the extracted windows.

See [RECONSTRUCTION.md](RECONSTRUCTION.md) for reproduction instructions and [DETAILS.md](DETAILS.md) for full selection, timing, and provenance details.

## Usage

```python
from datasets import load_dataset

benchmark = load_dataset("naivecynics/MetaScore-SFT-100", split="test")
assert len(benchmark) == 100

example = benchmark[0]
prompt = {"prompt": example["prompt"]}
reference = {"generation": example["reference"]}
```

For reproducible experiments, pin the dataset revision and keep generation settings fixed across systems.

**DAMN evaluation protocol:** generate eight continuations per prompt, compute each continuation's CLaMP3 cosine similarity to its reference, average across the eight candidates, then average across the 100 prompts. The 95% confidence intervals use bootstrap resampling over prompts. This is mean-of-eight, not best-of-eight. Reference similarity is a development proxy, not a substitute for listening evaluation.

## Citation

Please cite the original MetaScore work and this benchmark:

```bibtex
@inproceedings{xu2025metascore,
  title={Generating Symbolic Music from Natural Language Prompts Using an LLM-Enhanced Dataset},
  author={Xu, Weihan and McAuley, Julian and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo and Dong, Hao-Wen},
  booktitle={Proceedings of the International Society for Music Information Retrieval Conference},
  year={2025}
}

@misc{huang2026metascoresft100,
  title={MetaScore-SFT-100: A Fixed Piano Continuation Development Benchmark},
  author={Huang, Langchu and Xu, Yang},
  year={2026},
  howpublished={Hugging Face dataset},
  url={https://huggingface.co/datasets/naivecynics/MetaScore-SFT-100}
}
```

**Related resources:** [DAMN code](https://github.com/naivecynics/MIREX2026-DAMN) · [Model weights](https://huggingface.co/naivecynics/MIREX2026-DAMN) · [MIREX 2026 results](https://music-ir.org/mirex/wiki/2026:Symbolic_Music_Generation_Results) · [Listening demo](https://futuremirex.com/demos/symbolic-music-generation-2026/)

Maintainer: Langchu Huang ([langchuhuang@gmail.com](mailto:langchuhuang@gmail.com)).