在安装NVIDIA Container Toolkit之前，需确保已安装NVIDIA GPU驱动和Docker。根据操作系统使用相应命令安装工具包并配置Docker运行时。验证GPU支持后，可部署BGE-M3和BGE-Reranker模型，并执行命令启动服务进行测试。

Jina Reranker v2 is the best-in-class reranker built for Agentic RAG. It features function-calling support, multilingual retrieval for over 100 languages, code search capabilities, and offers a 6x...

With full integration of Jina Reranker, you can now bring Jina AI's state-of-the-art technology to SQL retrieval.

Learn how to use Jina Reranker and Embeddings with Haystack to create your own Jira ticket search engine, streamlining your operations and never again waste time creating duplicate issues.

You can build an article recommendation system with just the Jina Reranker API—no pipeline, no embeddings, no vector search, only reranking. Find out how in 20 lines of code.

Just Rerank It! Jina Reranker and LlamaIndex take your RAG up to the next level.

裁剪的小型语言模型在信息检索任务中表现出强大竞争力。压缩和稠密模型在实际应用中更受欢迎，但模型大小和早期查询文档交互对泛化能力有重要影响。增加模型大小对相同领域测试数据集几乎没有增益，但在新领域上有更大提高。reranker在几个任务中优于稠密模型。最大reranker在18个数据集中的12个达到最先进水平，平均超过以前最优结果3个点。域内有效性是零-shot有效性的好指标。

UPDATE: The pooling method for the Jina AI embeddings has been adjusted to use mean pooling, and the results have been updated accordingly. Notably, the JinaAI-v2-base-en with...

研究发现，裁剪后的小型语言模型在信息检索任务中表现出强大的竞争力，模型大小和早期查询文档交互对检索模型的泛化能力起着重要作用。此外，reranker在几个任务中大大优于其大小相似的稠密模型，最大reranker在Benchmark-IR（BEIR）的18个数据集中的12个数据集中达到了最先进水平。