全部模型 / SENTENCE-TRANSFORMERS

sentence-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 tasks. Priced at $0.005 per M tokens. 512 token context window.

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

API

快速调用

通过 OpenRouter 的 OpenAI 兼容 API 调用这个确切的模型 ID。

curl https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sentence-transformers/paraphrase-minilm-l6-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

frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p

可用提供方

1 提供方

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。

DeepInfra

unknown

上下文: 1K
最大输出: 0K
输入: $0.0050
输出: 免费
缓存读取: 暂未提供
缓存写入: 暂未提供

sentence-transformers

个模型

sentence-transformers

发布日期 2025年11月18日

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: $0.0050输出: 免费每百万 Token

查看模型详情 →

sentence-transformers

发布日期 2025年11月18日

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: $0.0050输出: 免费每百万 Token

查看模型详情 →

sentence-transformers

发布日期 2025年11月18日

Sentence Transformers: all-mpnet-base-v2

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...

上下文: 1K
输入: text
输出: embeddings

输入: $0.0050输出: 免费每百万 Token

查看模型详情 →

sentence-transformers

发布日期 2025年11月18日

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: $0.0050输出: 免费每百万 Token

查看模型详情 →