![]() ![]() To specify the column types in your dataset, create a JSON file named schema.json. To override column types in your dataset, supply a schema_overrides kwarg to studio.upload_dataset() in the following format:.To override the ID column, supply an id_column kwarg to studio.upload_dataset().Supported modalities include "text", "tabular", and "image" To override the dataset modality, supply a modality kwarg to studio.upload_dataset().We provide some options to override the inferred schema if necessary: Information on dataset structuring can be found by clicking the tutorial on ! Advanced Usage Schema Python APIĪll schema information will be inferred by default when uploading a dataset through the Python API. PySpark DataFrame (external media) (Python library only). ![]() Pandas DataFrame (external media) (Python library only).PySpark DataFrame (Python library only).Download your cleanset with cleanlab cleanset download.Ĭleanlab Studio supports the following upload types:.Improve your dataset in Cleanlab Studio (e.g., correct some labels).Upload your dataset (image, text, or tabular) using cleanlab dataset upload.If this is your first time using the Cleanlab CLI, authenticate with cleanlab login. ![]() download_cleanlab_columns ( ) corrected_dataset = studio. upload_dataset (, ) # navigate to Cleanlab Studio, create a project, and improve your labels # download your cleanset or apply corrections to your local Pandas or PySpark dataset! # you can find your cleanset ID by clicking on the Export Cleanset button in your project cleanset = studio. import cleanlab_studio # create your Cleanlab Studio API client with your API key, found here: studio = Studio ( ) # upload your dataset via a filepath, Pandas DataFrame, or PySpark DataFrame! dataset_id : str = studio. If you already have the client installed and wish to upgrade to the latest version, run: pip install -upgrade cleanlab-studio ![]() You can install the Cleanlab Studio client from PyPI with: pip install cleanlab-studio Upload datasets and download cleansets (cleaned datasets) from Cleanlab Studio in a single line of code! Command line and Python library interface to Cleanlab Studio. ![]()
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