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Our goal: make LAM datasets more discoverable and usable to support researchers, institutions, and ML practitioners working with cultural heritage data.
Cultural heritage data is often underrepresented in machine learning. BigLAM helps address this by:
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Supporting inclusive and responsible AI
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Helping institutions experiment with ML for access, discovery, and preservation
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Ensuring that ML systems reflect diverse human knowledge and expression
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Developing tools and methods that work well with the unique formats, values, and needs of LAMs
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AI & ML interests
🤗 Hugging Face x 🌸 BigScience initiative to create open source community resources for LAMs.
📚 BigLAM: Machine Learning for Libraries, Archives, and Museums
BigLAM is a community-driven initiative to build an open ecosystem of machine learning models, datasets, and tools for Libraries, Archives, and Museums (LAMs).
We aim to:
🗃️ Share machine-learning-ready datasets from LAMs via the Hugging Face Hub
🤖 Train and release open-source models for LAM-relevant tasks
🛠️ Develop tools and approaches tailored to LAM use cases
Our goal: make LAM datasets more discoverable and usable to support researchers, institutions, and ML practitioners working with cultural heritage data.
