Streamline dataset card; move technical details to companion documentation
Browse files- DETAILS.md +63 -0
- README.md +42 -101
- checksums.sha256 +2 -1
DETAILS.md
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# Additional benchmark details
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## Selection and fixed windows
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The existing selection pipeline retained 106 candidate scores satisfying:
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- A single piano part (`keyboard.piano*`), at least 16 measures, no lyrics.
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- The upstream best-unique-arrangement filter.
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- Rating >= 4.5 with at least 3 ratings.
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- The upstream 4/4 filter; the final MIDI window is checked explicitly against
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the continuation protocol.
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Manual listening rejected six clear quality-control failures; their CIDs are
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preserved in `summary.json`. The remaining 100 pieces span 15 genre labels.
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These genres are not equally represented; see the summary for exact counts.
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The deterministic window builder accounts for score pickup information,
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merges piano events and duplicate `(start, pitch)` notes, and chooses the first
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complete protocol-compatible 16-bar window. **99 pieces use the opening window;
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one uses the first subsequent compatible window** because the opening does not
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meet the protocol. `challenge_test/manifest.json` records the source SHA256,
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window index, pickup, selection reason, note counts, and MIDI parsing diagnostics.
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No new selection or window extraction was performed for this release.
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## Format
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Times use a sixteenth-note grid. A complete window ends at step 272, with steps
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0--15 reserved for an optional pickup. The four regular prompt bars occupy
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steps 16--79. Prompt notes have start in 0--79, and reference notes in 80--271.
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Pitch is an integer in 0--127; duration is a positive integer and notes do not
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extend beyond step 272. Notes are sorted by `(start, pitch)`.
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```json
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{"prompt": [{"start": 16, "pitch": 60, "duration": 4}]}
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```
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```json
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{"generation": [{"start": 80, "pitch": 64, "duration": 4}]}
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```
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The fixed-window MIDIs use 480 ticks per quarter note, 120 BPM, program 0 and
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velocity 64 for comparable listening. Original complete-piece assets retain
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their original contents. Genre labels and source previews are auxiliary;
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they are not additional generation conditions in the DAMN benchmark.
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## Provenance, permissions and limitations
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Metadata records 99 source scores as `cc-zero` and one as `publicdomain`.
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These are upstream declarations, not an independent verification of rights to
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every underlying composition or arrangement. Existing score credits and copyright
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notices are retained, and the explicit conflicting entry is included only in this private repository, without clearing its rights. There is
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no blanket license replacing source rights; see [LICENSE.md](LICENSE.md).
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The benchmark contains familiar repertoire and a small, curated selection of
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genres. Source metadata can be imperfect. References are one possible
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continuation rather than the only musically valid answer. Similarity to a
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reference is a development proxy, not a substitute for listening evaluation.
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The DAMN pipeline excludes benchmark matches from its training data; this does
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not establish the absence of overlap with other models' training corpora.
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README.md
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# MetaScore-SFT-100
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from [MetaScore](https://github.com/wx83/MetaScore_Official), used to evaluate
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[DAMN](https://github.com/naivecynics/MIREX2026-DAMN/tree/mirex2026-submission).
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**This is an independently curated development benchmark, not the official
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MIREX 2026 test set.** It contains one fixed 4-bar prompt (plus an optional pickup)
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and a 12-bar reference continuation for each piece.
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| --- | --- |
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| `test.jsonl` | 100
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| `challenge_test/
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of the fixed 16-bar evaluation windows. Source metadata spelling is retained.
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## Selection and fixed windows
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The existing selection pipeline retained 106 candidate scores satisfying:
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- A single piano part (`keyboard.piano*`), at least 16 measures, no lyrics.
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- The upstream best-unique-arrangement filter.
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-
-
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- The upstream 4/4 filter; the final MIDI window is checked explicitly against
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the continuation protocol.
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-
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Manual listening rejected six clear quality-control failures; their CIDs are
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preserved in `summary.json`. The remaining 100 pieces span 15 genre labels.
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These genres are not equally represented; see the summary for exact counts.
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-
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-
The deterministic window builder accounts for score pickup information,
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-
merges piano events and duplicate `(start, pitch)` notes, and chooses the first
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-
complete protocol-compatible 16-bar window. **99 pieces use the opening window;
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one uses the first subsequent compatible window** because the opening does not
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meet the protocol. `challenge_test/manifest.json` records the source SHA256,
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window index, pickup, selection reason, note counts, and MIDI parsing diagnostics.
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No new selection or window extraction was performed for this release.
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-
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## Format
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-
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-
Times use a sixteenth-note grid. A complete window ends at step 272, with steps
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| 85 |
-
0--15 reserved for an optional pickup. The four regular prompt bars occupy
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steps 16--79. Prompt notes have start in 0--79, and reference notes in 80--271.
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-
Pitch is an integer in 0--127; duration is a positive integer and notes do not
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extend beyond step 272. Notes are sorted by `(start, pitch)`.
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-
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```json
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{"prompt": [{"start": 16, "pitch": 60, "duration": 4}]}
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```
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velocity 64 for comparable listening. Original complete-piece assets retain
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their original contents. Genre labels and source previews are auxiliary;
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they are not additional generation conditions in the DAMN benchmark.
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## Usage
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```python
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from datasets import load_dataset
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assert len(benchmark) == 100
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```
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Pin the Hub revision for experiments. Use the same candidate count and sampling
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protocol when comparing results. DAMN's development evaluation generates eight
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candidates per prompt, averages their CLaMP3 cosine similarities to the reference
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within each prompt, and then averages across prompts; it does not select a
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reference-based best candidate. Confidence intervals resample prompts.
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These are upstream declarations, not an independent verification of rights to
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every underlying composition or arrangement. Existing score credits and copyright
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notices are retained, and the explicit conflicting entry is included only in this private repository, without clearing its rights. There is
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no blanket license replacing source rights; see [LICENSE.md](LICENSE.md).
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-
The
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genres. Source metadata can be imperfect. References are one possible
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-
continuation rather than the only musically valid answer. Similarity to a
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-
reference is a development proxy, not a substitute for listening evaluation.
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-
The DAMN pipeline excludes benchmark matches from its training data; this does
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not establish the absence of overlap with other models' training corpora.
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## Citation
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Please
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The DAMN manuscript is being prepared; no arXiv identifier is assigned here.
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```bibtex
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@inproceedings{xu2025metascore,
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booktitle={Proceedings of the International Society for Music Information Retrieval Conference},
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year={2025}
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}
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@misc{huang2026metascoresft100,
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title={MetaScore-SFT-100: A Fixed Piano Continuation Development Benchmark},
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author={Huang, Langchu and Xu, Yang},
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}
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```
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- [
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- [MetaScore paper](https://arxiv.org/abs/2410.02084)
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- [DAMN code](https://github.com/naivecynics/MIREX2026-DAMN/tree/mirex2026-submission)
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- [DAMN weights](https://huggingface.co/naivecynics/MIREX2026-DAMN)
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- [Official MIREX 2026 results](https://music-ir.org/mirex/wiki/2026:Symbolic_Music_Generation_Results)
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- [Official listening demo](https://futuremirex.com/demos/symbolic-music-generation-2026/)
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Maintainer: Langchu Huang (
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# MetaScore-SFT-100
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## Overview
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**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).
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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**.
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## Dataset
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The benchmark contains **100 complete examples across 15 genre labels**, with fixed prompts, references, and source assets.
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| Resource | Contents |
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| --- | --- |
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| [`test.jsonl`](test.jsonl) | 100 entries with CID, genre, source, and prompt/reference note sequences |
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| [`challenge_test/`](challenge_test) | Fixed prompt/reference JSON, companion MIDIs, and window manifest |
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| `midi/`, `mscz/`, `mp3/` | Complete-piece MIDI, MuseScore scores, and existing source previews |
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| [`label/`](label) | Genre annotations |
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| `manifest.csv`, `summary.json` | Source selection metadata and statistics |
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| [`attribution.json`](attribution.json) | Per-piece attribution and source license records |
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| [`checksums.sha256`](checksums.sha256) | File integrity checksums |
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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`.
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Source permissions apply per piece; see [LICENSE.md](LICENSE.md). This repository is private, and the existing source-license conflict remains documented in [rights_conflicts.json](rights_conflicts.json).
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## Construction
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We selected piano scores from a genre-stratified sample of MetaScore using these criteria:
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- A single piano part, 4/4 meter, at least 16 measures, and no lyrics.
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- The upstream best-unique-arrangement filter.
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- A user rating of at least 4.5 from at least three ratings.
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Automatic filtering retained 106 candidates; manual listening removed six clear quality-control failures, leaving 100 pieces. Genre counts are not balanced.
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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.
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See [RECONSTRUCTION.md](RECONSTRUCTION.md) for reproduction instructions and [DETAILS.md](DETAILS.md) for full selection, timing, and provenance details.
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## Usage
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```python
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from datasets import load_dataset
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# Authenticate with an account granted access to this private repository.
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benchmark = load_dataset("naivecynics/MetaScore-SFT-100", split="test", token=True)
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assert len(benchmark) == 100
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example = benchmark[0]
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prompt = {"prompt": example["prompt"]}
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reference = {"generation": example["reference"]}
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```
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For reproducible experiments, pin the dataset revision and keep generation settings fixed across systems.
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**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.
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## Citation
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Please cite the original MetaScore work and this benchmark:
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```bibtex
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@inproceedings{xu2025metascore,
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booktitle={Proceedings of the International Society for Music Information Retrieval Conference},
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year={2025}
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}
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@misc{huang2026metascoresft100,
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title={MetaScore-SFT-100: A Fixed Piano Continuation Development Benchmark},
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author={Huang, Langchu and Xu, Yang},
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}
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```
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**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/)
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Maintainer: Langchu Huang ([langchuhuang@gmail.com](mailto:langchuhuang@gmail.com)).
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fb17c2919d30257eb69f0399f9950eef45eeefb5076069a02043dc5e542008b5 LICENSE.md
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-
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3d7fb3d8ea12c6f947ad493b8ad7acd0b409d1ffeb8f0edfa6e6bd633b579760 RECONSTRUCTION.md
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343ab1ca9de0ad1388e326aba00574963099eb56419a1f5ed169f1cc530efe9e attribution.json
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dd0b867a5f593be3d463c7e0414209f7cd8bd68ca3d93099b165585a18cde5d5 challenge_test/manifest.json
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9dacb0089cb22df76f57bebc55bc127efc95633312e973ab08cc1f2c7e6a4824 DETAILS.md
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fb17c2919d30257eb69f0399f9950eef45eeefb5076069a02043dc5e542008b5 LICENSE.md
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5b5f6c5c195841f017a93fb2c02d9212a3d7d553a7ad55b62a3a62cc2e1cb355 README.md
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3d7fb3d8ea12c6f947ad493b8ad7acd0b409d1ffeb8f0edfa6e6bd633b579760 RECONSTRUCTION.md
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343ab1ca9de0ad1388e326aba00574963099eb56419a1f5ed169f1cc530efe9e attribution.json
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dd0b867a5f593be3d463c7e0414209f7cd8bd68ca3d93099b165585a18cde5d5 challenge_test/manifest.json
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