The Hub is home to an extensive collection of community-curated and popular research datasets. We encourage you to share your dataset to the Hub to help grow the ML community and accelerate progress for everyone. All contributions are welcome; adding a dataset is just a drag and drop away!
Start by creating a Hugging Face Hub account if you don’t have one yet.
The Hub’s web-based interface allows users without any developer experience to upload a dataset.
A repository hosts all your dataset files, including the revision history, making storing more than one dataset version possible.
Adding a Dataset card is super valuable for helping users find your dataset and understand how to use it responsibly.
README.md
file in your repository.Feel free to copy this Dataset card template to help you fill out all the relevant fields.
The Dataset card uses structured tags to help users discover your dataset on the Hub. Use the Dataset Tagger to help you generate the appropriate tags.
Copy the generated tags, paste them at the top of your Dataset card, and then commit your changes.
For a detailed example of what a good Dataset card should look like, take a look at the CNN DailyMail Dataset card.
Once your dataset is stored on the Hub, anyone can load it with the load_dataset() function:
>>> from datasets import load_dataset
>>> dataset = load_dataset("stevhliu/demo")
Users who prefer to upload a dataset programmatically can use the huggingface_hub library. This library allows users to interact with the Hub from Python.
pip install huggingface_hub
huggingface-cli login
push_to_hub()
function to help you add, commit, and push a file to your repository:>>> from datasets import load_dataset
>>> dataset = load_dataset("stevhliu/demo")
# dataset = dataset.map(...) # do all your processing here
>>> dataset.push_to_hub("stevhliu/processed_demo")
To set your dataset as private, set the private
parameter to True
. This parameter will only work if you are creating a repository for the first time.
>>> dataset.push_to_hub("stevhliu/private_processed_demo", private=True)
A private dataset is only accessible by you. Similarly, if you share a dataset within your organization, then members of the organization can also access the dataset.
Load a private dataset by providing your authentication token to the use_auth_token
parameter:
>>> from datasets import load_dataset
# Load a private individual dataset
>>> dataset = load_dataset("stevhliu/demo", use_auth_token=True)
# Load a private organization dataset
>>> dataset = load_dataset("organization/dataset_name", use_auth_token=True)
Congratulations, you’ve completed the tutorials! 🥳
From here, you can go on to:
If you have any questions about 🤗 Datasets, feel free to join and ask the community on our forum.