Datasets:
Document verified SQL queries and reproducible dataset revision
Browse files- README.md +10 -6
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README.md
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### Load a configuration reproducibly
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```python
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from datasets import load_dataset
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repo = "EnvLoop/Recursive-Play-Bench"
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revision =
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models = load_dataset(repo, name="models", split="test", revision=revision)
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families = load_dataset(repo, name="families", split="test", revision=revision)
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depths = load_dataset(repo, name="depths", split="test", revision=revision)
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### Query an aggregate in Data Studio
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Open
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```sql
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SELECT primaryRank, displayName, fullWins, fullWinRatePercent,
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levelsCompleted, levelClearRatePercent
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FROM
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ORDER BY displayOrder;
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```
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```sql
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SELECT environmentId, family, maximumCompletedLevelsAcrossEleven,
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astraCompletedLevels, referenceTotalActions
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FROM
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ORDER BY maximumCompletedLevelsAcrossEleven,
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referenceTotalActions, environmentId;
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```
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The [native SQL Console](https://huggingface.co/docs/hub/main/en/datasets-viewer-sql-console) can share query results and export tables. The [Data Studio guide](https://huggingface.co/docs/hub/en/data-studio) explains filtering, search, missing-value distributions and row sharing. These facilities inspect the recorded outcomes; they do not run a solver or create a new benchmark score.
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## Files and configurations
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### Load a configuration reproducibly
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The aggregate views are available at the recorded commit `e519f64ccc67fb4fc32a393b9f3a77597432336c`. Passing this hash loads the published summary tables reproducibly. Later card-only revisions do not change the data at this commit. You can inspect other revisions under [Files and versions](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/tree/main).
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```python
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from datasets import load_dataset
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repo = "EnvLoop/Recursive-Play-Bench"
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revision = "e519f64ccc67fb4fc32a393b9f3a77597432336c"
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models = load_dataset(repo, name="models", split="test", revision=revision)
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families = load_dataset(repo, name="families", split="test", revision=revision)
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depths = load_dataset(repo, name="depths", split="test", revision=revision)
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### Query an aggregate in Data Studio
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Open [models/test](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/models/test), then open its SQL Console. Hugging Face registers this selected view as `models`, which is the table name used below. The query follows the paper's recorded display order and retains tied ranks.
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```sql
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SELECT primaryRank, displayName, fullWins, fullWinRatePercent,
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levelsCompleted, levelClearRatePercent
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FROM models
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ORDER BY displayOrder;
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```
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Open the saved [Ranking and Model Performance query](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/sql-console/aul_Tu_) to inspect the same eleven rows directly in the native SQL Console.
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The SQL Console also lists the challenge view as `challenges`. This query reproduces the paper's challenge ordering using the task-level completion and reference-action fields:
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```sql
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SELECT environmentId, family, maximumCompletedLevelsAcrossEleven,
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astraCompletedLevels, referenceTotalActions
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FROM challenges
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ORDER BY maximumCompletedLevelsAcrossEleven,
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referenceTotalActions, environmentId;
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```
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The saved [Challenge Levels and Actions query](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/sql-console/jGND0-1) opens these fifteen challenge rows in the same order.
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The [native SQL Console](https://huggingface.co/docs/hub/main/en/datasets-viewer-sql-console) can share query results and export tables. The [Data Studio guide](https://huggingface.co/docs/hub/en/data-studio) explains filtering, search, missing-value distributions and row sharing. These facilities inspect the recorded outcomes; they do not run a solver or create a new benchmark score.
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## Files and configurations
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SHA256SUMS.txt
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-
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362ae6fca185ef2e595653558c4f0f96bf7e95585c5152d20df75adedd070031 challenge-inventory.json
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89e64b2b1b2e43fedd2c984ff70e41c1048bbcc0eb959f3c868dc9eeccaea104 challenge-tasks.jsonl
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5261f6c28b14ebd3e9a5a10139729c717b2792d24ab363713bd3381220273696 completion-depths.jsonl
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f7b578e359312c2603aacfb886701224c507695183fc06d7aedf426b9d04f330 README.md
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362ae6fca185ef2e595653558c4f0f96bf7e95585c5152d20df75adedd070031 challenge-inventory.json
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89e64b2b1b2e43fedd2c984ff70e41c1048bbcc0eb959f3c868dc9eeccaea104 challenge-tasks.jsonl
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5261f6c28b14ebd3e9a5a10139729c717b2792d24ab363713bd3381220273696 completion-depths.jsonl
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