🤗 Datasets is a lightweight library providing two main features:
squad_dataset = load_dataset("rajpurkar/squad"), get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX),processed_dataset = dataset.map(process_example), efficiently prepare the dataset for inspection and ML model evaluation and training.🎓 Documentation 🔎 Find a dataset in the Hub 🌟 Share a dataset on the Hub
🤗 Datasets is designed to let the community easily add and share new datasets.
🤗 Datasets has many additional interesting features:
🤗 Datasets originated from a fork of the awesome TensorFlow Datasets and the HuggingFace team want to deeply thank the TensorFlow Datasets team for building this amazing library.
🤗 Datasets can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance)
🤗 Datasets can be installed using conda as follows:
conda install -c huggingface -c conda-forge datasets
Follow the installation pages of TensorFlow and PyTorch to see how to install them with conda.
For more details on installation, check the installation page in the documentation: https://huggingface.co/docs/datasets/installation
Installation to use with Machine Learning & Data frameworks frameworksIf you plan to use 🤗 Datasets with PyTorch (2.0+), TensorFlow (2.6+) or JAX (3.14+) you should also install PyTorch, TensorFlow or JAX. 🤗 Datasets is also well integrated with data frameworks like PyArrow, Pandas, Polars and Spark, which should be installed separately.
For more details on using the library with these frameworks, check the quick start page in the documentation: https://huggingface.co/docs/datasets/quickstart
🤗 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.
This library can be used for text/image/audio/etc. datasets. Here is an example to load a text dataset:
Here is a quick example:
from datasets import load_dataset
# Print all the available datasets
from huggingface_hub import list_datasets
print([dataset.id for dataset in list_datasets()])
# 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"])})
# Process the dataset - 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)
If your dataset is bigger than your disk or if you don't want to wait to download the data, you can use streaming:
# If you want to use the dataset immediately and efficiently stream the data as you iterate over the dataset
image_dataset = load_dataset('timm/imagenet-1k-wds', streaming=True)
for example in image_dataset["train"]:
break
For more details on using the library, check the quick start page in the documentation: https://huggingface.co/docs/datasets/quickstart and the specific pages on:
We have a very detailed step-by-step guide to add a new dataset to the datasets already provided on the HuggingFace Datasets Hub.
You can find:
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!
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.
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