Model Card for Astro-HEP-BERT

Astro-HEP-BERT is a bidirectional transformer designed primarily to generate contextualized word embeddings for computational conceptual analysis in astrophysics and high-energy physics (HEP). Built upon Google's bert-base-uncased, the model underwent additional training for three epochs using 21.84 million paragraphs found in more than 600,000 scholarly articles sourced from arXiv, all pertaining to astrophysics and/or high-energy physics (HEP). The sole training objective was masked language modeling.

The Astro-HEP-BERT project demonstrates the general feasibility of training a customized bidirectional transformer for computational conceptual analysis in the history, philosophy, and sociology of science as an open-source endeavor that does not require a substantial budget. Leveraging only freely available code, weights, and text inputs, the entire training process was conducted on a single MacBook Pro Laptop (M2/96GB).

For further insights into the model, the corpus, and the underlying research project (Network Epistemology in Practice) please refer to the Astro-HEP-BERT paper [link coming soon].

Model Details

• Developer: Arno Simons
• Funded by: The European Union under Grant agreement ID: 101044932
• Language (NLP): English
• License: apache-2.0
• Parent model: Google's bert-base-uncased