datasets README

The project's README from github.com/huggingface/datasets. As one markdown file: README.md.

Hugging Face Datasets Library

Build GitHub Documentation GitHub release Number of datasets Contributor Covenant DOI

🤗 Datasets is a lightweight library providing two main features:

🎓 Documentation 🔎 Find a dataset in the Hub 🌟 Share a dataset on the Hub

🚀 Key Features

🤗 Datasets is designed to let the community easily add and share new datasets, and provides powerful capabilities for data manipulation:

Feature Description
📦 One-line dataset loading Load AI-ready datasets from the Hugging Face Hub or local files with load_dataset()
🔍 Multiple formats Native support for CSV, JSON, JSONL, Parquet, Arrow, XML, Text, Webdataset, and more
🖼️ Multi-modal data Built-in support for text, audio, image, video, PDF, and NIfTI (3D medical) data
🚀 Streaming mode Stream datasets without downloading — iterate over data on-the-fly with streaming=True (now up to 100x faster with Xet backend)
💾 HF Storage Buckets Read and write directly from/to Hugging Face Storage Buckets for mutable, large-scale raw data
🧠 AI Agent Traces Load and process AI agent traces (prompts, tool calls, responses) from the Hub
⚡ Apache Arrow backend Zero-copy memory-mapped storage — datasets naturally free you from RAM limitations
🔄 Smart caching Never wait for your data to process twice — cached results are automatically reused
📊 Multi-framework interoperability Native conversion to/from NumPy, Pandas, Polars, Arrow, PyTorch, TensorFlow, JAX, and Spark
🏎️ Multi-processing Fast parallel data processing with map(num_proc=N)
🔎 Search & index Built-in FAISS and Elasticsearch index support for similarity search
📦 JSON type Flexible JSON/structured data support with Json() feature type

Installation

With pip

🤗 Datasets can be installed from PyPi and should be installed in a virtual environment (venv or conda for instance):

pip install datasets

For the latest development version:

pip install "datasets @ git+https://github.com/huggingface/datasets.git"

With conda

conda install -c huggingface -c conda-forge datasets

Optional dependencies

🤗 Datasets supports various optional features via extras:

# For audio (torchcodec)
pip install datasets[audio]

# For image/video (Pillow, torchcodec)
pip install datasets[vision]

# For PDFs/NIfTI (pdfplumber, nibabel)
pip install datasets[pdfs,nibabel]

# For PyTorch/TensorFlow/JAX integration
pip install datasets[torch,tensorflow,jax]

For more details on installation, check the installation page.

Quick Start

🤗 Datasets is made to be very simple to use — the API is centered around a single function, datasets.load_dataset(dataset_name, **kwargs), that instantiates a dataset.

Here is a quick example:

from datasets import load_dataset

# Load a dataset and print the first example in the training set
squad_dataset = load_dataset('rajpurkar/squad')
print(squad_dataset['train'][0])

# Process the dataset - add a column with the length of the context texts
dataset_with_length = squad_dataset.map(lambda x: {"length": len(x["context"])})

# Tokenize the context texts (using a tokenizer from the 🤗 Transformers library)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')

tokenized_dataset = squad_dataset.map(lambda x: tokenizer(x['context']), batched=True)

# Tokenize chat conversations with a chat template (using a model that supports chat templates)
# This is useful for fine-tuning instruction/chat models

# Load a popular chat dataset (ultrachat_200k contains ~200k AI assistant conversations)
chat_dataset = load_dataset('HuggingFaceH4/ultrachat_200k', split='train_sft')

chat_tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-7B-Instruct')

def tokenize_chat(examples):
    # Apply the chat template and tokenize in one step
    return chat_tokenizer.apply_chat_template(examples["messages"])

tokenized_chat_dataset = chat_dataset.map(tokenize_chat, batched=True)

Streaming mode

If your dataset is bigger than your disk or if you don't want to wait to download the data, you can use streaming:

# Stream the dataset without downloading anything
image_dataset = load_dataset('timm/imagenet-1k-wds', streaming=True)
for example in image_dataset["train"]:
    print(example["image"])
    break

Multi-modal data

🤗 Datasets supports a wide variety of data types out of the box:

# Audio dataset
dataset = load_dataset("openslr/librispeech_asr", "clean")

# Image dataset
dataset = load_dataset("ILSVRC/imagenet-1k")

# Video dataset
dataset = load_dataset("Shofo/shofo-tiktok-general-small")

# PDF documents
dataset = load_dataset("pixparse/pdfa-eng-wds")

# NIfTI (3D medical imaging)
dataset = load_dataset("dartbrains/localizer", "betas")

From local files

# Load from local CSV
dataset = load_dataset('csv', data_files='my_data.csv')

# Load from local Parquet
dataset = load_dataset('parquet', data_files='data/*.parquet')

# Load from a local directory (auto-detect format)
dataset = load_dataset('./path/to/data')

From Python objects

from datasets import Dataset

# From a dictionary
dataset = Dataset.from_dict({"text": ["Hello world", "How are you?"]})

# From a list
dataset = Dataset.from_list([{"text": "Hello world"}, {"text": "How are you?"}])

# From Pandas
import pandas as pd
df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
dataset = Dataset.from_pandas(df)

# From a generator
def gen():
    for i in range(10):
        yield {"value": i}
dataset = Dataset.from_generator(gen)

For more details on using the library, check the quick start guide and the specific pages on:

Core Classes

The library provides two main dataset classes:

Class Description
Dataset In-memory / memory-mapped dataset backed by Apache Arrow. Supports indexing, slicing, random access and caching.
IterableDataset Lazy, streamable dataset for large-scale / out-of-core processing. Supports streaming and infinite iteration.

Both are wrapped in DatasetDict / IterableDatasetDict for multi-split datasets (e.g., train/test/val).

Add a new dataset to the Hub

We have a very detailed step-by-step guide to add a new dataset to the number of datasets datasets already provided on the HuggingFace Datasets Hub.

You can find:

Disclaimers

You can use 🤗 Datasets to load datasets based on versioned git repositories maintained by the dataset authors. For reproducibility reasons, we ask users to pin the revision of the repositories they use.

If you're a dataset owner and wish to update any part of it (description, citation, license, etc.), or do not want your dataset to be included in the Hugging Face Hub, please get in touch by opening a discussion or a pull request in the Community tab of the dataset page. Thanks for your contribution to the ML community!

Contributing

We welcome contributions! Please see our Contributing Guide for details on:

BibTeX

If you want to cite our 🤗 Datasets library, you can use our paper:

@inproceedings{lhoest-etal-2021-datasets,
    title = "Datasets: A Community Library for Natural Language Processing",
    author = "Lhoest, Quentin  and
      Villanova del Moral, Albert  and
      Jernite, Yacine  and
      Thakur, Abhishek  and
      von Platen, Patrick  and
      Patil, Suraj  and
      Chaumond, Julien  and
      Drame, Mariama  and
      Plu, Julien  and
      Tunstall, Lewis  and
      Davison, Joe  and
      {\v{S}}a{\v{s}}ko, Mario  and
      Chhablani, Gunjan  and
      Malik, Bhavitvya  and
      Brandeis, Simon  and
      Le Scao, Teven  and
      Sanh, Victor  and
      Xu, Canwen  and
      Patry, Nicolas  and
      McMillan-Major, Angelina  and
      Schmid, Philipp  and
      Gugger, Sylvain  and
      Delangue, Cl{\'e}ment  and
      Matussi{\`e}re, Th{\'e}o  and
      Debut, Lysandre  and
      Bekman, Stas  and
      Cistac, Pierric  and
      Goehringer, Thibault  and
      Mustar, Victor  and
      Lagunas, Fran{\c{c}}ois  and
      Rush, Alexander  and
      Wolf, Thomas",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-demo.21",
    pages = "175--184",
    abstract = "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.",
    eprint={2109.02846},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
}

If you need to cite a specific version of our 🤗 Datasets library for reproducibility, you can use the corresponding version Zenodo DOI from this list.

datasets (huggingface/datasets)

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22.1K ·GitHub
Datasets
What is this?A lightweight Python library from Hugging Face that gives one-line dataloaders for a huge catalog of ready-to-use public datasets on the HF Hub, plus fast, reproducible preprocessing for Hub and local data.
Is this a website or a library I install?A library: you install it with pip or conda into your Python environment. The datasets themselves live on the Hugging Face Hub — the library pulls them in as Arrow-backed Dataset objects, in memory or streamed.
How do I get it running?pip install datasets in a virtualenv or conda env (a conda package and the dev version are available too), then load data in one line, e.g. load_dataset('rajpurkar/squad').
Do I need PyTorch or TensorFlow installed to use it?No — nothing beyond Python is required, and datasets convert natively to and from NumPy, Pandas, Polars, Arrow, PyTorch, TensorFlow, JAX and Spark, so you can feed whichever training stack you already use.
Do I need a beefy machine for the big datasets?No — the Apache Arrow backend uses zero-copy memory-mapped storage, so datasets are not limited by your RAM, and for data bigger than your disk there is streaming=True, which iterates on-the-fly without downloading.
Is this aimed at ML researchers, or regular Python developers too?Anyone training or evaluating ML models — it hands you data ready for a training or eval dataloader, and it is built so the community can easily add and share new datasets.
Will it run on my Windows laptop, or does it need a Linux server?It runs wherever Python runs — it is a plain pip package with nothing OS-specific in it, so Windows, macOS and Linux all work.
Can I use it on my own local files, or only Hub datasets?Your own files work the same way: CSV, JSON, JSONL, Parquet, HDF5, XML, text, PNG, JPEG, WAV, MP3, PDF and NIfTI are supported, plus plain Python objects — all going through the same map()-based preprocessing.
What kinds of data can it handle?Text (datasets in 467 languages and dialects), images, audio, video, PDFs and NIfTI 3D medical scans, plus AI agent traces; multi-split data comes back as a DatasetDict with train/test/val.
Does it reprocess my data every time I run a script?No — processed results are cached and automatically reused, so expensive map() work happens once; map(num_proc=N) can also parallelize it across processes.
How do I publish my own dataset to the Hub?There is a detailed step-by-step guide: upload via web browser or Python, or push with Git; dataset owners can update or remove their dataset anytime via the Community tab of its page.
What are similar projects?transformers, ailearning, d2l-en, Ai-Learn, keras, DeepLearning, transformers.js, autumn-recruitment
Which topics is it in?medical-imaging, polars, jax, spark, speech, datasets, pandas, elasticsearch, numpy, vector-search, tensorflow, caching
Creator
huggingface
Size
113.7 MB
Stars
22.1K
Updated
CommitOct 16Release5.1.0Oct 05
Age
6y
Commits · 90 Days