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name: datasets description: "Use this skill when working with Hugging Face Datasets: loading local or Hub datasets, defining Features schemas, processing/streaming datasets, converting formats, sharing to the Hub, managing cache/offline behavior, or using datasets-cli." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Hugging Face Datasets

Use this repo skill for practical work with the datasets Python package. It is self-contained guidance distilled from the Datasets source, docs, tests, and verified public API surface.

Start Here

• Install the package with pip install datasets; add extras only for workflows that need them, such as datasets[audio], datasets[vision], datasets[pdfs], datasets[nibabel], or ML framework packages.
• Run python -c "from datasets import Dataset, load_dataset; print(Dataset.from_dict({'x':[1]}))" for a minimal import/API smoke check.
• Use load_dataset(...) for Hub datasets, local files, local dataset repositories, and packaged format builders.
• Use Dataset.from_dict, Dataset.from_list, Dataset.from_pandas, or Dataset.from_generator when data already exists in Python.

Route by Task

• Load datasets from the Hub, local files/directories, Python objects, generators, packaged modules, or disk snapshots with sub-skills/loading-local-hub/SKILL.md.
• Transform, filter, batch, split, shuffle, stream, format, combine, or convert datasets with sub-skills/processing-streaming/SKILL.md.
• Define Features, cast columns, troubleshoot schemas, and work with CSV/JSON/Parquet/Arrow/text/image/audio/video/PDF/NIfTI/mesh formats with sub-skills/features-formats/SKILL.md.
• Push datasets to the Hub, create dataset cards, use datasets-cli, manage cache/offline mode, or diagnose filesystem/storage issues with sub-skills/sharing-cli-cache/SKILL.md.

Shared References and Scripts

• Read references/api-surface.md for the verified top-level API map and the most important signatures.
• Read references/troubleshooting.md for cross-cutting install, import, optional dependency, auth, cache, and workflow routing failures.
• Read references/repo-provenance.md when checking whether this skill is stale relative to a Datasets checkout.
• Run scripts/datasets_api_smoke.py --help for a safe local smoke helper that verifies import, core APIs, tiny in-memory processing, and CLI availability.

Common Decisions

• Prefer explicit data_files, split, and features when automatic local file inference would be ambiguous.
• Use streaming=True for large or remote data that should not be fully downloaded; route downstream iterable semantics to processing-streaming.
• Use decode=False for media columns when optional decoders are unavailable or when you only need paths/bytes metadata.
• Do not put tokens in code, logs, dataset cards, or generated examples; pass tokens through normal Hugging Face auth mechanisms.
• Treat delete_from_hub and push_to_hub as state-changing operations; dry-run the plan and confirm repo IDs/config names before running them.

Validation Pattern

1. Confirm the package imports and the relevant optional dependency is installed.
2. Build or load a tiny representative dataset first.
3. Declare Features when schema inference is risky.
4. Run the intended map/filter/cast/format operation on a tiny subset.
5. Save, reload, or push only after the local smoke path behaves as expected.