🤗Datasets is tested on Python 3.6+.

You should install 🤗Datasets in a virtual environment. If you’re unfamiliar with Python virtual environments, check out the user guide. Create a virtual environment with the version of Python you’re going to use and activate it.

Now, if you want to use 🤗Datasets, you can install it with pip. If you’d like to play with the examples, you must install it from source.

Installation with pip¶

🤗Datasets can be installed using pip as follows:

pip install datasets

To check 🤗Datasets is properly installed, run the following command:

python -c "from datasets import load_dataset; print(load_dataset('squad', split='train')[0])"

It should download version 1 of the Stanford Question Answering Dataset, load its training split and print the first training example:

{'answers': {'answer_start': [515], 'text': ['Saint Bernadette Soubirous']}, 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.', 'id': '5733be284776f41900661182', 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?', 'title': 'University_of_Notre_Dame'}

If you want to use the 🤗Datasets library with TensorFlow 2.0 or PyTorch, you will need to install these seperately. Please refer to TensorFlow installation page and/or PyTorch installation page regarding the specific install command for your platform.

Installing from source¶

To install from source, clone the repository and install with the following commands:

git clone
cd datasets
pip install -e .

Again, you can run:

python -c "from datasets import load_dataset; print(load_dataset('squad', split='train')[0])"

to check 🤗Datasets is properly installed.

With conda¶

🤗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.

Caching datasets and metrics¶

This library will download and cache datasets and metrics processing scripts and data locally.

Unless you specify a location with cache_dir=... when you use methods like load_dataset and load_metric, these datasets and metrics will automatically be downloaded in the folders respectively given by the shell environment variables HF_DATASETS_CACHE and HF_METRICS_CACHE. The default value for it will be the HuggingFace cache home followed by /datasets/ for datasets scripts and data, and /metrics/ for metrics scripts and data.

The HuggingFace cache home is (by order of priority):

  • shell environment variable HF_HOME

  • shell environment variable XDG_CACHE_HOME + /huggingface/

  • default: ~/.cache/huggingface/

So if you don’t have any specific environment variable set, the cache directory for dataset scripts and data will be at ~/.cache/huggingface/datasets/.