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244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
38 lines
1.0 KiB
Python
38 lines
1.0 KiB
Python
"""SpreadsheetBench task dataloader."""
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from __future__ import annotations
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from skillopt.datasets.base import SplitDataLoader
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class SpreadsheetBenchDataLoader(SplitDataLoader):
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"""SpreadsheetBench dataloader.
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Each split directory contains a .json file (JSON array of task items).
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Spreadsheet files referenced by items live under a separate ``data_root``.
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"""
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def __init__(
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self,
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split_dir: str = "",
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data_path: str = "",
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split_mode: str = "ratio",
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split_ratio: str = "2:1:7",
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split_seed: int = 42,
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split_output_dir: str = "",
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data_root: str = "",
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seed: int = 42,
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limit: int = 0,
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**kwargs,
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) -> None:
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super().__init__(
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split_dir=split_dir,
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data_path=data_path,
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split_mode=split_mode,
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split_ratio=split_ratio,
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split_seed=split_seed,
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split_output_dir=split_output_dir,
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seed=seed,
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limit=limit,
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)
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self.data_root = data_root
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