Paper · arXiv 2408.09574
PhysBERT: A Text Embedding Model for Physics Scientific Literature
Thorsten Hellert, João Montenegro, Andrea Pollastro
PhysBERT is a physics-specific text embedding model pre-trained on arXiv physics papers, surpassing general-purpose models in physics-related tasks and fine-tuning for subdomains.
Abstract
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.