Choosing machine learning models
Posted: Thu Jul 10, 2025 3:59 am
Guest Post by Daniel Van Strien, Machine Learning Librarian, Hugging Face
Machine learning has many potential applications for working with GLAM (galleries, libraries, archives, museums) collections, though it is not always clear how to get started. This post outlines some of the possible ways in which open source machine learning tools from the Hugging Face ecosystem can be used to explore web archive collections made available via the Internet Archive’s ARCH (Archives Research Compute Hub). ARCH aims to make computational work with web archives more accessible by streamlining web archive data access, visualization, analysis, and sharing. Hugging Face is focused on the democratization of good machine learning. A key component of this is not only making models available but also doing extensive work around the ethical use of machine learning.
Below, I work with the Collaborative Art Archive (CARTA) collection focused on artist websites. This post is accompanied by an ARCH Image Dataset Explorer Demo. The goal of this post is to show how using a specific set of open source machine learning models can help you explore a large dataset through image search, image classification, and model training.
Later this year, Internet Archive and Hugging Face will organize a hands-on hackathon focused on using open source machine learning tools with web archives. Please let us know if you are interested in participating by filling out this form.
The Hugging Face Hub is a central repository which provides access to open phone number list source machine learning models, datasets and demos. Currently, the Hugging Face Hub has over 150,000 openly available machine learning models covering a broad range of machine learning tasks.
Rather than relying on a single model that may not be comprehensive enough, we’ll select a series of models that suit our particular needs.
A screenshot of the Hugging Face hub task navigator presenting a way of filtering machine learning models hosted on the hub by the tasks they intend to solve. Example tasks are Image Classification, Token Classification and Image-to-Text.
A screenshot of the Hugging Face Hub task navigator presenting a way of filtering machine learning models hosted on the hub by the tasks they intend to solve. Example tasks are Image Classification, Token Classification and Image-to-Text.
Working with image data
ARCH currently provides access to 16 different “research ready” datasets generated from web archive collections. These include but are not limited to datasets containing all extracted text from the web pages in a collection, link graphs (showing how websites link to other websites), and named entities (for example, mentions of people and places). One of the datasets is made available as a CSV file, containing information about the images from webpages in the collection, including when the image was collected, when the live image was last modified, a URL for the image, and a filename.
Machine learning has many potential applications for working with GLAM (galleries, libraries, archives, museums) collections, though it is not always clear how to get started. This post outlines some of the possible ways in which open source machine learning tools from the Hugging Face ecosystem can be used to explore web archive collections made available via the Internet Archive’s ARCH (Archives Research Compute Hub). ARCH aims to make computational work with web archives more accessible by streamlining web archive data access, visualization, analysis, and sharing. Hugging Face is focused on the democratization of good machine learning. A key component of this is not only making models available but also doing extensive work around the ethical use of machine learning.
Below, I work with the Collaborative Art Archive (CARTA) collection focused on artist websites. This post is accompanied by an ARCH Image Dataset Explorer Demo. The goal of this post is to show how using a specific set of open source machine learning models can help you explore a large dataset through image search, image classification, and model training.
Later this year, Internet Archive and Hugging Face will organize a hands-on hackathon focused on using open source machine learning tools with web archives. Please let us know if you are interested in participating by filling out this form.
The Hugging Face Hub is a central repository which provides access to open phone number list source machine learning models, datasets and demos. Currently, the Hugging Face Hub has over 150,000 openly available machine learning models covering a broad range of machine learning tasks.
Rather than relying on a single model that may not be comprehensive enough, we’ll select a series of models that suit our particular needs.
A screenshot of the Hugging Face hub task navigator presenting a way of filtering machine learning models hosted on the hub by the tasks they intend to solve. Example tasks are Image Classification, Token Classification and Image-to-Text.
A screenshot of the Hugging Face Hub task navigator presenting a way of filtering machine learning models hosted on the hub by the tasks they intend to solve. Example tasks are Image Classification, Token Classification and Image-to-Text.
Working with image data
ARCH currently provides access to 16 different “research ready” datasets generated from web archive collections. These include but are not limited to datasets containing all extracted text from the web pages in a collection, link graphs (showing how websites link to other websites), and named entities (for example, mentions of people and places). One of the datasets is made available as a CSV file, containing information about the images from webpages in the collection, including when the image was collected, when the live image was last modified, a URL for the image, and a filename.