Aravind Srinivas· @AravSrinivas · X·· 2 小时前AI 评分50
AI 导读
Perplexity 开源其上下文嵌入模型，在 turbopuffer 的 context-bench 上表现最佳。被引用内容介绍其新训练方法让每个文档分块在整篇文档语境下编码，pplx-embed-v2-context-9b-preview 在 ConTEB 和 turbopuffer 的 context-bench 上刷新纪录，详情见 perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage。
正文
We’re open sourcing our state-of-the-art contextual embedding models, which perform best in turbopuffer’s context-bench.
We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view. pplx-embed-v2-context-9b-preview sets a new state of the art on ConTEB and @turbopuffer's new, privately held context-bench. https://www.perplexity.ai/hub/blog/contextual-embedding-beyond-the-gold-passage在 X 查看被引用的帖子
来源：Aravind Srinivas · x.com