Data is better together: Enabling communities to collectively build better datasets together using Argilla and Hugging Face Spaces

Recently, Argilla and Hugging Face launched Data is Better Together, an experiment to collectively build a preference dataset of prompt rankings. In a few days, we had:

• 350 community contributors labeling data
• Over 11,000 prompt ratings

See the progress dashboard for the latest stats!

This resulted in the release of 10k_prompts_ranked, a dataset consisting of 10,000 prompts with user ratings for the quality of the prompt. We want to enable many more projects like this!

In this post, we’ll discuss why we think it’s essential for the community to collaborate on building datasets and share an invitation to join the first cohort of communities Argilla and Hugging Face will support to develop better datasets together!

Data remains essential for better models

Data continues to be essential for better models: We see continued evidence from published research, open-source experiments, and from the open-source community that better data can lead to better models.

The question.

A frequent answer.

Why build datasets collectively?

Data is vital for machine learning, but many languages, domains, and tasks still lack high-quality datasets for training, evaluating, and benchmarking — the community already shares thousands of models, datasets, and demos daily via the Hugging Face Hub. As a result of collaboration, the open-access AI community has created amazing things. Enabling the community to build datasets collectively will unlock unique opportunities for building the next generation of datasets to build the next generation of models.

Empowering the community to build and improve datasets collectively will allow people to:

• Contribute to the development of Open Source ML with no ML or programming skills required.
• Create chat datasets for a particular language.
• Develop benchmark datasets for a specific domain.
• Create preference datasets from a diverse range of participants.
• Build datasets for a particular task.
• Build completely new types of datasets collectively as a community.