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Datasets

open source / Overall score: 4.6(leading)

Datasets is a lightweight Hugging Face library providing one-line dataloaders for thousands of public datasets and efficient preprocessing for machine-learning workflows. It supports CSV, JSON, Parquet, audio, image and video formats with streaming and memory-mapping, and integrates with PyTorch, TensorFlow and JAX. It is the loading layer that feeds training and evaluation pipelines.

Openness

5 high confidence

5.0

license: Apache-2.0(OSI)
source: public
core features withheld: no

The LICENSE file is the standard, unmodified Apache 2.0 text. The repository is public, and the README describes the whole library with no paid, enterprise or hosted tier beside it, so source is public and the core ungated.

https://cdn.jsdelivr.net/gh/huggingface/datasets@main/LICENSE recorded 2026-09-29
LICENSE file is the Apache License text, headed "Version 2.0, January 2004", with "TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION" and no added terms.
https://cdn.jsdelivr.net/gh/huggingface/datasets@main/README.md recorded 2026-09-29
README gives the development install from the public repository, "datasets @ git+https://github.com/huggingface/datasets.git", for the whole library. Searched the body for enterprise, pricing, paid and license key: none.
https://github.com/huggingface/datasets/blob/main/LICENSE recorded 2026-08-13
LICENSE file is the verbatim Apache License Version 2.0 text
https://pypi.org/pypi/datasets/json recorded 2026-09-29
PyPI metadata lists "License :: OSI Approved :: Apache Software License" and the license field "Apache 2.0".

Adoption

5 high confidence

5.0

Adoption is measured as PyPI downloads of the datasets package in the trailing 30 days.

https://pypistats.org/api/packages/datasets/recent recorded 2026-08-13
last_month downloads = 159,958,389 for datasets

Capability

4 high confidence

4.0

Broad and central to data pipelines but scoped to data handling rather than modeling.

https://cdn.jsdelivr.net/gh/huggingface/datasets@main/README.md recorded 2026-09-29
README describes "one-line dataloaders for many public datasets" and "efficient data pre-processing", with streaming, "Apache Arrow backend" memory-mapped storage and "Multi-framework interoperability" across NumPy, Pandas, Polars, PyTorch, TensorFlow and JAX.
https://github.com/huggingface/datasets recorded 2026-08-13
README still describes one-line dataloaders and efficient preprocessing across text, image, audio, video and 3D medical data with streaming

Verified 2026-09-24