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sentence-transformers

Sentence Transformers: all-mpnet-base-v2

来源介绍（英文）

The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and...

在 OpenRouter 查看来源 Hugging Face
模型概览

模型规格

上下文
512 tokens
最大输出
460 tokens
架构
text->embeddings
分词器
Other
知识截止时间
暂未提供
内容审核
否
OPENROUTER

完整价格

同步自 OpenRouter。Token 价格均按每百万 Token 展示。

输入
$0.005
每百万 Token
API

快速调用

在本地设置 OPENROUTER_API_KEY。Python 需安装 requests；JavaScript 在 Node.js 中运行。密钥应保存在服务端。

API 文档

curl --fail-with-body https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sentence-transformers/all-mpnet-base-v2","input":"Your text here"}'
能力与模态

输入 → 输出

输入
text
输出
embeddings
API

支持的 API 参数

可用提供方

1 提供方

核验日期: 2026年9月16日

在 OpenRouter 查看实时 Provider

Provider 可用性、延迟、吞吐量和路由会持续变化，请前往来源页面查看实时运行数据。

OpenRouter

DeepInfra

暂未提供
上下文
1K
最大输出
0K
输入
$0.005
输出
免费
缓存读取
暂未提供
缓存写入
暂未提供
sentence-transformers

4 个模型

全部模型
sentence-transformers logosentence-transformers

Sentence Transformers: paraphrase-MiniLM-L6-v2

The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval...

上下文
1K
输入
text
输出
embeddings
输入: $0.005每百万 Token
查看模型详情 →
sentence-transformers logosentence-transformers

Sentence Transformers: all-MiniLM-L12-v2

The all-MiniLM-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and...

上下文
1K
输入
text
输出
embeddings
输入: $0.005每百万 Token
查看模型详情 →
sentence-transformers logosentence-transformers

Sentence Transformers: multi-qa-mpnet-base-dot-v1

The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.

上下文
1K
输入
text
输出
embeddings
输入: $0.005每百万 Token
查看模型详情 →
sentence-transformers logosentence-transformers

Sentence Transformers: all-MiniLM-L6-v2

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering,...

上下文
1K
输入
text
输出
embeddings
输入: $0.005每百万 Token
查看模型详情 →