Hugging Face Datasets is an open-source Python library for loading, processing and sharing datasets for text, image and audio AI, often in one line of code.
In plain words
Before training, you need data in a usable shape. Datasets can download a dataset from the Hugging Face Hub with one call, then filter, transform and stream it. It stores data in the Apache Arrow format, which is organised by column and read directly from disk through memory-mapping, so large datasets do not have to fit in your computer's memory. It hands data to tools such as NumPy, Pandas and PyTorch without copying.
A quick example
Imagine you want thousands of movie reviews to train a sentiment model. You call the library's load function with the dataset's name from the Hub, apply a tokenizer to every row, and feed the result to your PyTorch training loop.
Where you'll meet it
- Preparing training data for language, vision and audio models.
- Sharing datasets on the Hugging Face Hub.
Related
Sources
- DocsDatasets, Hugging Face documentation · read 29 Sept 2026
- DocsDatasets and Arrow, Hugging Face documentation · read 29 Sept 2026
- Docshuggingface/datasets, Hugging Face (GitHub) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.