Text Ranking
sentence-transformers
Safetensors
Korean
English
qwen3
cross-encoder
reranker
korean
english
Instructions to use nlpai-lab/KURE-Reranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nlpai-lab/KURE-Reranker-base with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("nlpai-lab/KURE-Reranker-base") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
File size: 741 Bytes
65bf9cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {%- set instruction = messages | selectattr("role", "eq", "system") | map(attribute="content") | first | default("Given a web search query, retrieve relevant passages that answer the query") -%}
{%- set query_text = messages | selectattr("role", "eq", "query") | map(attribute="content") | first -%}
{%- set document_text = messages | selectattr("role", "eq", "document") | map(attribute="content") | first -%}
<|im_start|>system
Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>
<|im_start|>user
<Instruct>: {{ instruction }}
<Query>: {{ query_text }}
<Document>: {{ document_text }}<|im_end|>
<|im_start|>assistant
<think>
</think>
|