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AI 模型库/nasa-impact/indus-sde-st-v0.2
MODEL PROFILE · 模型档案

indus-sde-st-v0.2

nasa-impact · indus-sde-st-v0.2

> **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.

译文排队中，当前展示来源原文。

向量与相似度license:apache-2.0
上下文窗口来源未披露
输入 → 输出文本 → 向量
模型权重待核验
档案更新时间2026/10/31 05:38:17
OVERVIEW

模型介绍

> **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.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用？

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗？

不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

huggingface · nasa-impact/indus-sde-st-v0.2 ↗2026/10/30 14:56:20
huggingface · enrich:41647795-62f2-41bd-96e9-e160053953f4 ↗2026/10/31 05:38:17

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。