Jina AI(@JinaAI_)
v5-text uses decoder-only backbones with last-token pooling instead of mean pooling. Four lightweigh...
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TL;DR · AI 摘要
Jina AI 发布 v5-text 模型，采用 decoder-only 架构与 last-token pooling，并集成四个轻量 LoRA 适配器支持多任务。
核心要点
- v5-text 使用 decoder-only 主干网络和 last-token pooling 替代传统 mean pooling
- 每个 Transformer 层注入四个轻量 LoRA 适配器，分别处理检索、匹配、分类和聚类
- 支持 32K 上下文长度，是 v3 的 4 倍，并在推理时动态选择适配器
#LoRA#大模型#文本嵌入#Jina AI#Transformer
打开原文Jina AI on X: "v5-text uses decoder-only backbones with last-token pooling instead of mean pooling. Four lightweight LoRA adapters are injected at each transformer layer, handling retrieval, text-matching, classification, and clustering independently. Users select the appropriate adapter at https://t.co/j4RESCgE7S" / X
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v5-text uses decoder-only backbones with last-token pooling instead of mean pooling. Four lightweight LoRA adapters are injected at each transformer layer, handling retrieval, text-matching, classification, and clustering independently. Users select the appropriate adapter at inference time. For retrieval, queries get a "Query:" prefix and documents get "Document:". Context length is 32K tokens, a 4x increase over v3.
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