feat: add generic Hugging Face dataset converter

[joseph-isaacs]

Summary

• add a resumable converter for Parquet shards in any Hugging Face dataset repository
• support Parquet Zstd, Vortex, and compact Vortex outputs with deterministic destination paths
• require Xet-backed source and destination repositories and expose bounded Xet download concurrency
• harden plan/apply orchestration, bounded pipelines, live telemetry, retries, and checkpoint compatibility
• batch Hub commits, verify downloads, and delete local outputs only after successful commits
• add reliability and regression coverage for both converter implementations
• add scripts/ as a non-package uv workspace project with declared converter dependencies

Validation

• uv run --project scripts python -m unittest scripts/tests/test_hf_parquet_convert.py (23 tests, 1 skipped)
• uv run --project scripts python -m unittest scripts/tests/test_hf_parquet_convert_v2.py (23 tests, 1 skipped)
• uv run --project scripts scripts/huggingface-convert.py --help
• git diff --check

The converter has also been exercised against a large FineWeb upload, but it contains no FineWeb-specific repository or path assumptions.

[codspeed-hq]

Merging this PR will improve performance by 35.78%

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

⚡ 2 improved benchmarks
✅ 2155 untouched benchmarks
⏩ 106 skipped benchmarks¹
🗄️ 4 archived benchmarks run²

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	WallTime	arrow_checked_add_u32_neon[16384]	20.5 µs	13.3 µs	+53.3%
⚡	WallTime	arrow_checked_add_u32_avx2[16384]	21.3 µs	17.7 µs	+20.27%

Tip

Curious why performance improved? Comment @codspeedbot explain why performance improved on this PR, or directly use the CodSpeed MCP with your agent.

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_{Comparing ji/hf-dataset-vortex-converter (0366a8a) with develop (68e2aee)}

Footnotes

1. 106 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

2. 4 benchmarks were run, but are now archived. If they were deleted in another branch, consider rebasing to remove them from the report. Instead if they were added back, click here to restore them. ↩