hf-dataset-push

name	hf-dataset-push
description	Push a prepared dataset to Hugging Face Hub as a Dataset repository, with dataset card (README.md), config, and data files (Parquet / JSONL / CSV). Use after the dataset is cleaned and packaged (ideally via the parquet-jsonl-package skill) and the user wants it published on HF.

Hugging Face Dataset Push

Publish a dataset to huggingface.co/datasets/<user-or-org>/<name>.

When to invoke

• User asks to "push to Hugging Face", "publish dataset to HF", "upload to the Hub".
• Dataset is already cleaned, documented (data dictionary), and packaged (Parquet / JSONL / CSV).

Prerequisites

• Hugging Face account and username (or org).
• Authenticated CLI: hf auth login (or huggingface-cli login on older versions) — if not authenticated, stop and instruct the user to log in. Do not prompt for or store tokens.
• Packaged files ready — if only raw CSV exists, suggest running parquet-jsonl-package first for better HF Datasets integration.

Procedure

1. Gather metadata from the user:
   • Repo name (kebab-case on HF).
   • Namespace: personal user or org.
   • Visibility: public or private.
   • License (SPDX identifier — e.g. mit, apache-2.0, cc-by-4.0, cc0-1.0).
   • Task tags (e.g. text-classification, tabular-classification, question-answering).
   • Language(s) if applicable.
   • Size category (auto-infer from row count: n<1K, 1K<n<10K, 10K<n<100K, 100K<n<1M, 1M<n<10M, n>10M).
2. Generate the dataset card (README.md) with YAML frontmatter:

   ---
   license: <spdx>
   task_categories:
     - <task>
   language:
     - en
   size_categories:
     - 10K<n<100K
   tags:
     - <tag>
   ---
   

   Followed by sections: Description, Source, Data fields (copied from the data dictionary), Splits (if any), Preprocessing / provenance (from the data dictionary transformations log), Licensing, Citation.
3. Organise files:
   • Root: README.md (dataset card), data_dictionary.md (copy of the data dictionary).
   • data/ folder: train.parquet / test.parquet / validation.parquet if the user has splits — otherwise a single data.parquet or data.jsonl.
   • If using HF's auto-detection for splits, name files by split pattern (train-*.parquet).
4. Create the repo:

   hf repo create <namespace>/<name> --type dataset [--private]
   

5. Upload files using hf upload (preferred) or huggingface_hub.HfApi().upload_folder(...):

   hf upload <namespace>/<name> ./local-folder --repo-type dataset
   

   For large files, hf upload-large-folder handles resumable multi-part uploads.
6. Verify — fetch the repo page, confirm files are listed, dataset card renders, and the "Dataset Preview" loads. If preview fails, inspect schema and file naming.
7. Report the dataset URL to the user.

Dependencies

pip install huggingface_hub
# The `hf` CLI is installed with huggingface_hub >= 0.24. Older installs use `huggingface-cli`.

Edge cases

• Not authenticated — stop and tell the user to run hf auth login. Do not handle tokens in code.
• Private dataset with org — confirm the user has write permission to the org.
• Files > 5 GB — use hf upload-large-folder or enable LFS via hf lfs configuration.
• Dataset already exists — ask whether to push a new revision (commit), create a new branch, or abort. Do not force-push without explicit confirmation.
• Sensitive data — before upload, verify with the user that no PII / credentials / private information is included. Public HF datasets are crawled and indexed.
• License mismatch — if the source data has a license that restricts redistribution, warn the user and confirm before proceeding.