Model overview
> **Paper:** INDUS-SDE: A Language Model for Scientific Content Curation and Discovery — **KDD 2026, AI for Sciences Track**. INDUS-SDE-ST is a sentence transformer for semantic scientific discovery, built on the INDUS-SDE encoder. > **Code:** NASA-IMPACT/st-training-workflow · **Binary (EQAT) variant:** indus-sde-st-equat-v0.1 The model was first further fine tuned on sentence embedding task on top of previous (nasa-impact/indus-sde-st-v0.1) using stage 2 dataset (scientific dataset) for a epoch. Then this model is again fined tuned for 2 more epoches on NASA SDE and NASA ADS corpus. The initial stage of Indus-SDE-ST training focused on adapting the base Indus-SDE model to comprehend general domain semantics and sentence-pair relationships. The stage 2 dataset was designed for scieinfic domain adaptation. The primary objective was to establish a broad linguistic foundation before specializing in scientific content (for subsequent stages). This was achieved using a diverse corpus comprising pairs from S2ORC, arxiv, PubMed, NASA ADS and NASA SDE set in a contrastive learning objective: Multiple Negatives Ranking loss.
Key features
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Limitations & considerations
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Source prices & cost analysis
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。