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IBM, NASA Team Up to Build Transformer-Based Language Models for Scientific Community

IBM, NASA Team Up to Build Transformer-Based Language Models for Scientific Community
Language models

IBM has teamed up with NASA to build a suite of transformer architecture-based language models designed to give the scientific and academic community access to vast amounts of scientific knowledge and information.

The IBM-NASA models were trained on 60 billion tokens from a collection of astrophysics, earth science, planetary science, heliophysics and biological and physical sciences data.

Members of the scientific and academic community can use the open-source version of the models available on Hugging Face to support various use cases, such as data classification, entity extraction, question answering and information retrieval.

The trained encoder model can be modified to support non-generative linguistic tasks and can generate data embeddings for document retrieval using retrieval augmented generation.

The IBM-NASA team also developed a retriever model on top of the encoder model to produce embeddings that map the similarity between text pairs.

IBM’s Watsonx.ai, a geospatial foundation model built from NASA’s satellite data, is also available on Hugging Face to expand the use of Earth science data for geospatial intelligence and advance climate-related innovation.

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Written by Naomi Cooper

is a full-time writer for ArchIntel, a division of Executive Mosaic, and a contributor for ExecutiveBiz. She covers emerging technologies, technology innovation, contract awards, partnerships and market expansion stories in the government contracting industry.

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