datasets
HuggingFace community-driven open-source library of datasets
Decision gist · record as of 2026-08-14
Yes. Datasets is production-stable, actively maintained, has no known vulnerabilities, and low install friction. It's the standard way to load and preprocess data from the Hugging Face Hub and local files in the ML ecosystem. Install it if you work with datasets for machine learning, data exploration, or preprocessing—it will save time and reduce boilerplate.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction: pure Python wheel with 14 runtime dependencies (numpy, pyarrow, pandas, requests, huggingface-hub, and others) that are widely available.
- Active maintenance with a release 17 days ago and recent commits.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use without restriction, making it suitable for any project type.
last release 2026-07-28 (17 days) · last repo commit 2026-08-12 · 21,837 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 138,759,474 downloads/mo, #279 on PyPI
Alternatives
Verify before relying
pip install datasets
from datasets import load_dataset
squad_dataset = load_dataset('rajpurkar/squad')
processed = squad_dataset.map(lambda x: {"length": len(x["context"])})- Performance characteristics of streaming mode with different backends and dataset sizes.
- Memory overhead of caching for very large datasets.
- Compatibility details with optional extras (audio, vision, pdfs, nibabel) not listed as runtime deps.
What it is and what it does
Datasets is a library for loading, preprocessing, and converting datasets from the Hugging Face Hub or local files. It handles many formats natively—CSV, JSON, Parquet, Arrow, audio, image, video, PDF, and NIfTI—and provides a simple `load_dataset()` function to fetch public datasets ready for machine learning. The library uses Apache Arrow as its backend for memory-efficient, zero-copy storage that frees you from RAM limits.
Once loaded, datasets support efficient preprocessing through a `map()` function for applying transformations, smart caching to avoid reprocessing, and multi-processing for speed. You can stream datasets without downloading them entirely, convert to NumPy, Pandas, Polars, PyTorch, TensorFlow, JAX, or Spark, and search using FAISS or Elasticsearch indexes. It's designed for the ML workflow: load a dataset, preprocess it, and feed it directly into training or evaluation.
Use it for
- Download and preprocess a public dataset like SQuAD for fine-tuning a language model with one line of code.
- Stream a large image or audio dataset without waiting for full download, iterating on-the-fly during training.
- Convert a local CSV or Parquet file into a PyTorch DataLoader for model training.
- Apply a tokenization or augmentation function across a dataset in parallel using `map(num_proc=N)`.
- Load multi-modal data (text, audio, images) from the Hub and convert to a framework-agnostic format for exploration.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Datasets is production-stable, actively maintained, has no known vulnerabilities, and low install friction. It's the standard way to load and preprocess data from the Hugging Face Hub and local files in the ML ecosystem. Install it if you work with datasets for machine learning, data exploration, or preprocessing—it will save time and reduce boilerplate.
Install
datasets on PyPI
Before you install
Low friction: pure Python wheel with 14 runtime dependencies (numpy, pyarrow, pandas, requests, huggingface-hub, and others) that are widely available. Active maintenance with a release 17 days ago and recent commits.
Requires Python 3.10 or later.
License in practice
Apache 2.0 permissive license allows commercial and private use without restriction, making it suitable for any project type.
Quickstart
pip install datasets
from datasets import load_dataset
squad_dataset = load_dataset('rajpurkar/squad')
processed = squad_dataset.map(lambda x: {"length": len(x["context"])})Verify before relying
- Performance characteristics of streaming mode with different backends and dataset sizes.
- Memory overhead of caching for very large datasets.
- Compatibility details with optional extras (audio, vision, pdfs, nibabel) not listed as runtime deps.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 14 packagesfilelocknumpypyarrowdillpandasrequestshttpxtqdmxxhashmultiprocessfsspechuggingface-hubpackagingpyyaml |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 138,759,474 / month, #279 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: datasets-5.0.1-py3-none-any.whl
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