PhysBERT: A Text Embedding Model for Physics Scientific Literature
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.
66c5a926e8f01fe34ec01d7d
Thorsten Hellert
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2024-08-21T08:45:27.429Z
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Thorsten  Hellert
user
thellert
66c5a926e8f01fe34ec01d7e
João Montenegro
66c5a926e8f01fe34ec01d7f
Andrea Pollastro
PhysBERT: A Text Embedding Model for Physics Scientific Literature
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.
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.
text embedding model NLP PhysBERT arXiv physics papers fine-tuning physics-specific tasks