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
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