AstroBERT Small: Domain-specialized small models

AstroBERT Small is a new series of domain-specialized models that demonstrate strong performance despite their compact size, with only 22.7 million parameters. These models are specifically trained on ArXiv abstracts in the astro-ph category and astronomy-related Wikipedia articles, showing that a focused domain approach can outperform much larger generalized models. The series includes a base language model and a sentence-transformers model designed for embeddings, with training methods like masked language modeling and distillation from larger models. Evaluation against other models, including the 600M parameter Qwen3 Embeddings model, reveals AstroBERT Small's competitive edge in terms of performance and efficiency, making it suitable for CPU-only setups without significant accuracy trade-offs. This initiative highlights the potential of small models in specialized domains, offering a cost-effective and space-efficient alternative to larger counterparts.

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