The Hugging Face Hub, best known as a repository for machine learning models and datasets, is emerging as a valuable resource for galleries, libraries, archives, and museums (GLAM). This guide explains how cultural heritage professionals can leverage the Hub to share collections, discover AI tools, and build interactive demos.
What is the Hugging Face Hub?
The Hugging Face Hub is a central platform where the machine learning community shares models, datasets, and demo applications. It hosts over 190,000 models, 33,000 datasets, and 100,000 applications, spanning tasks like text classification, image recognition, and generative AI. The Hub is free to use and encourages open collaboration.
Key Components for GLAM
- Models: Pre-trained AI models for tasks like optical character recognition (OCR), image annotation, and language translation. Many models can be fine-tuned on cultural heritage data.
- Datasets: Over 30,000 datasets, including collections of historical texts, photographs, and audio recordings. GLAM institutions can upload their own datasets for public use.
- Spaces: A platform to host interactive demos and web apps. Institutions can create a Space to showcase a model’s predictions or provide a public interface for exploring their collections.
Finding Relevant Models
To find models suitable for GLAM work, use the Hub’s search filters. For example, search for "OCR" or "historical document" and filter by task (e.g., image-to-text). Many models are openly licensed and can be used directly or fine-tuned.
Walkthrough: Adding a GLAM Dataset
- Create a new dataset repository: Log in to Hugging Face, click “New dataset,” and fill in the name and license.
- Upload files: Drag and drop your data files (CSV, JSON, images, etc.) or use the CLI for large datasets.
- Add metadata: Include a README with description, source, language, and intended use. Use tags like 'GLAM' for discoverability.
- Preview: The Hub automatically generates a preview for tabular data and images.
- Alternative methods: Use the
huggingface_hubPython library to programmatically upload datasets.
Why GLAM Institutions Should Use the Hub
- Visibility: Share datasets and models with a global community of researchers and developers.
- Collaboration: Engage with AI experts who can help improve models or create applications.
- Reproducibility: Host versioned datasets and models alongside code, ensuring research can be reproduced.
- Low Barrier: Free hosting and simple upload process, even for non-technical team members.
Examples from Leading Institutions
- BigLAM: A collaborative project that aggregates GLAM datasets on the Hub for large-scale analysis.
- National Library of Norway (AI Lab): Shares OCR models and historical newspaper datasets.
- Smithsonian Institution: Provides datasets of museum collections, enabling image classification and object detection research.
Hub Features for GLAM
- Dataset Viewer: Explore tabular data and images directly in the browser.
- Version Control: Every upload is tracked, allowing rollback or branching for experimentation.
- Community Tools: Discussion forums, issue tracking, and model cards provide transparency.
Getting Help
The Hugging Face community is active on the Hub’s discussion forums and Discord server. For GLAM-specific guidance, check the 'GLAM' tag on the Hub or reach out to the author via the blog post comments.
By embracing the Hugging Face Hub, cultural heritage institutions can unlock the power of AI while fostering open science and public engagement.