PhysBERT: A Text Embedding Model for Physics Scientific Literature
Authors: name: Thorsten Hellert, name: João Montenegro, name: Andrea Pollastro

The specialized language and complex concepts in physics pose significant
challenges for information extraction through Natural Language Processing
(NLP). Central to effective NLP applications is the text embedding model, which
converts text into dense vector representations for efficient information
retrieval and semantic analysis. In this work, we introduce PhysBERT, the first
physics-specific text embedding model. Pre-trained on a curated corpus of 1.2
million arXiv physics papers and fine-tuned with supervised data, PhysBERT
outperforms leading general-purpose models on physics-specific tasks including
the effectiveness in fine-tuning for specific physics subdomains.