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  1. Industry AI Model Platform / 
  2. v1.x / 
  3. Datasets / 
  4. Update Datasets

Documentation

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    Update Datasets

    Edit Dataset Files

    Local Editing (Git / SDK)

    Clone the dataset repository locally via Git or Python SDK, make changes to files, then push the updates to the remote.

    Using Git:

    # Clone the repository
git lfs install
git clone https://<username>:<access-token>@<platform-host>/<namespace>/<dataset-name>
cd <dataset-name>

# Edit files, then commit and push
git add .
git commit -m "update dataset files"
git push

    Using Python SDK:

    from pycsghub.repository import Repository

repo = Repository(
    repo_id="<namespace>/<dataset-name>",
    repo_type="dataset",
    endpoint="https://<platform-host>",
    token="<access-token>"
)

repo.clone()

# Edit files in the local repository, then push
repo.push()

    Web Online Editing

    1. Navigate to the dataset detail page and switch to the Files tab
    2. Click the file name you want to edit to open the file viewer
    3. Click the Edit button to enter online editing mode
    4. Modify the file content, enter a commit message, and click Commit to save

    Tip

    Web online editing is suitable for modifying text files such as README.md or configuration files. For large data file updates, use Git or CLI instead.

    Repository Settings

    Navigate to the dataset detail page and click the Settings tab to modify repository configuration.

    Non-modifiable Fields

    The following fields cannot be changed after creation:

    Field Description
    Dataset Name The English identifier of the dataset; fixed once created
    Visibility Defaults to private; the platform does not currently support self-service visibility changes via the UI

    Modifiable Fields

    Field Description
    Nickname Update the friendly display name for the dataset
    Description Update the text description of the dataset
    Task Tags Add or modify task type tags (e.g., text-classification, question-answering)
    Industry Tags Add or modify industry domain tags (e.g., finance, healthcare, education)

    Delete a Dataset

    Warning

    Deleting a dataset is irreversible. All data files and version history will be permanently removed and cannot be recovered.

    To delete a dataset:

    1. Navigate to the dataset detail page and click the Settings tab
    2. Scroll to the Delete Dataset section at the bottom of the page
    3. Type the full name of the dataset in the confirmation input field
    4. Click the Delete button to confirm