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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.

Ver fonte no OpenRouter Hugging Face
Visão geral

Especificações do modelo

Contexto
512 tokens
Saída máxima
460 tokens
Arquitetura
text->embeddings
Tokenizador
Other
Limite de conhecimento
Não informado
Moderado
Não
OPENROUTER

Preços completos

Tarifas sincronizadas do OpenRouter, por milhão de tokens.

Entrada
$0.005
/M tokens
API

Início rápido

Use este modelo pela API compatível com OpenAI do OpenRouter.

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"}'
Recursos e modalidades

Entrada → Saída

Entrada
text
Saída
embeddings
API

Parâmetros de API compatíveis

frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Provedores disponíveis

1 Provedor

Provedores ao vivo no OpenRouter

Disponibilidade, latência, throughput e roteamento mudam continuamente. Consulte a fonte para dados atuais.

OpenRouter

DeepInfra

unknown
Contexto
1K
Saída máxima
0K
Entrada
$0.0050
Saída
Grátis
Leitura de cache
Não informado
Gravação de cache
Não informado
sentence-transformers

modelos

Todos os modelos
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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...

Contexto
1K
Entrada
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Saída
embeddings
Entrada: $0.0050Saída: Grátispor milhão de tokens
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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.

Contexto
1K
Entrada
text
Saída
embeddings
Entrada: $0.0050Saída: Grátispor milhão de tokens
Ver detalhes do modelo →
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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...

Contexto
1K
Entrada
text
Saída
embeddings
Entrada: $0.0050Saída: Grátispor milhão de tokens
Ver detalhes do modelo →
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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,...

Contexto
1K
Entrada
text
Saída
embeddings
Entrada: $0.0050Saída: Grátispor milhão de tokens
Ver detalhes do modelo →