Cleanlab automatically finds and fixes dataset errors, tags issues in multi-label data, and evaluates language model outputs for reliability.
What it is
Cleanlab is a data quality and machine learning platform designed to detect errors in structured, text, image, and document datasets. It provides specialized tools including Cleanlab Studio for automated data curation and the Trustworthy Language Model (TLM) for reliable information extraction and scoring.
Key features
- Automatically score data quality and flag erroneous values in tabular, text, and image datasets
- Identify label errors, missing values, and unusual combinations of column values across databases
- Assign trustworthiness scores to language model outputs to catch hallucinations and extraction errors
- Format and format-check multi-label classification datasets and image tags
- Integrate programmatically via Python API or use the web interface
Who it's for
Data scientists, machine learning engineers, and developers who need to clean training data, audit datasets, and ensure reliability in language model pipelines and automated data extraction tasks.
Pricing
Check the official website for current pricing.
Use cases
- Find data entry errors in tabular datasets
- Detect annotation issues in multi-label image datasets
- Extract information from unstructured PDF documents
- Impute missing values in structured data tables
Categories:
Similar to Cleanlab
More Cleanlab alternatives
Collaborate on machine learning models and datasets
Hugging Face is a collaborative hub for machine learning practitioners to build, share, and deploy models, datasets, and applications.
