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loading-local-hub【数据与研究】Load datasets from the Hub, local files, packaged builders, Python objects, generators, and disk snapshots while choosing safe offline or authenticated network paths.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the task is to get data into datasets before transformation: Hub repositories, local files/directories, packaged modules such as csv or json, Python-native objects, generator functions, or snapshots previously written with save_to_disk.
load_dataset("namespace/name", name=..., split=..., revision=..., token=...); read loading workflows for revision, token, and offline boundaries.csv, json, parquet, text, imagefolder, or audiofolder with explicit data_files or data_dir; read loading workflows.load_dataset_builder(...) to inspect configs, features, and splits before materializing rows; read API reference.load_from_disk(dataset_path, storage_options=...) for directories produced by save_to_disk; read API reference.Dataset.from_dict, Dataset.from_list, Dataset.from_pandas, Dataset.from_generator, or DatasetDict; read API reference.data_files, and uncached revisions as network operations; avoid them unless the user expects network access.token=True only when the user has authenticated locally; pass a token value only from a secret manager or environment variable.revision for reproducibility and explicit data_files mappings for deterministic splits.../processing-streaming/SKILL.md.../features-formats/SKILL.md.datasets-cli, route to ../sharing-cli-cache/SKILL.md.Run scripts/create_local_loading_fixture.py to create a tiny local multi-split CSV fixture and optional example snippets. This is safe and self-contained; it does not import datasets, access the network, or depend on the source checkout.
Read troubleshooting when loading fails due to missing files, ambiguous split inference, config selection, private Hub auth, offline mode, local format inference, load_from_disk path mistakes, or storage_options for remote filesystems.