Aravind Srinivas· @AravSrinivas · X·· 2 小时前AI 评分63
AI 导读
Perplexity 开源 pplx-embed-v2-late，两个 late-interaction 多向量嵌入模型，支持文本、图像和 PDF 页面检索，共享同一嵌入空间，权重已在 Hugging Face 上发布。9B 版本用于索引多模态数据，0.6B 版本可在设备端查询，检索 PDF 页面无需 OCR；在 MADQA 上得分 92.4%，BrowseComp+ 上得分 64%。
正文
We’re open-sourcing pplx-embed-v2-late, multi-vector embeddings for text and images, 9B and 0.6B, in one shared embedding space. You can use these to index multimodal data with 9B, and query on device with 0.6B. This also enables you to search over PDF pages with no OCR. And scores 92.4% on MADQA, 64% on BrowseComp+. Weights available on @huggingface now.
We're releasing pplx-embed-v2-late, two late-interaction embedding models that retrieve text, images, and pages with a shared embedding space for cross-model querying. Both models achieve frontier performance and are publicly available on Hugging Face. https://www.perplexity.ai/hub/blog/multimodal-embeddings-beyond-a-single-vector在 X 查看被引用的帖子
来源：Aravind Srinivas · x.com