Edit model card

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

Downloads last month
13
Safetensors
Model size
110M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train arnosimons/astro-hep-bert