huggingface-git-xet-dataset-publisher

You can publish large Hugging Face datasets by using Git Xet/LFS-compatible tracking to manage large artifacts safely. This involves binding a local non-Git folder to a remote repository, installing Git Xet, and configuring track rules in .gitattributes before committing and pushing to the main branch.

The best way to bootstrap a Hugging Face dataset repository from a local folder is to bind it to a remote repository, install Git Xet, and configure .gitattributes track rules. This process ensures your local data is safely version-controlled and ready for snapshot commits.

To recover from binary rejection when pushing to a Hugging Face dataset repository, you can use Git Xet/LFS-compatible tracking to reindex stages and validate artifacts. This ensures large binaries are correctly tracked in .gitattributes before attempting a safe push validation.

You can maintain README and .gitattributes track rules by synchronizing repository configuration across tracked file patterns. This ensures that your track rules are consistently updated in both files whenever you perform daily snapshot commits or update your dataset.

Yes, you can perform daily snapshot updates for Hugging Face datasets using Git Xet. The process performs preflight checks, stage reindexing, and safe push validation to ensure your data history remains reproducible and large artifacts are safely committed.

Before pushing Hugging Face dataset repositories with Git Xet, you need to configure the repo_dir, hf_remote, branch, and a set of file patterns to track. These parameters allow the process to perform preflight checks, stage reindexing, and safe push validation.