We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
text
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:
new_line
new_line
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
text
1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
new_line
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
text
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
new_line
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
text
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.
new_line
new_line
Try it out: 
text
Try it out: 
https://huggingface.co/zilliz/semantic-highlight-bilingual-v1
zilliz/semantic-highlight-bilingual-v1
model
resource
https://huggingface.co/zilliz/semantic-highlight-bilingual-v1
new_line
Read our article: 
text
Read our article: 
https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
link
new_line
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:

1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.

Try it out: https://huggingface.co/zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model

🔥
unmodeled-tyler wangbuer999 zc277584121 DervinA1 PachoNVD software-si John6666 umair-dev SSSilvio altavista87
🚀
AnsoATC Lollo001lollo zc277584121