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sentence-transformers

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 text ranking. Priced at $0.005 per M tokens. 512 token context window.

Ver fuente en OpenRouter Hugging Face
Resumen

Especificaciones del modelo

Contexto
512 tokens
Salida máxima
460 tokens
Arquitectura
text->embeddings
Tokenizador
Other
Corte de conocimiento
No indicado
Moderado
No
OPENROUTER

Precios completos

Tarifas sincronizadas desde OpenRouter, por millón de tokens.

Entrada
$0.005
/M tokens
API

Inicio rápido

Usa este modelo mediante la API compatible con OpenAI de OpenRouter.

curl 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"}'
Capacidades y modalidades

Entrada → Salida

Entrada
text
Salida
embeddings
API

Parámetros API compatibles

frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Proveedores disponibles

1 Proveedor

Proveedores en vivo en OpenRouter

Disponibilidad, latencia, rendimiento y enrutamiento cambian continuamente. Consulta la fuente para datos actuales.

OpenRouter

DeepInfra

unknown
Contexto
1K
Salida máxima
0K
Entrada
$0.0050
Salida
Gratis
Lectura de caché
No indicado
Escritura de caché
No indicado
sentence-transformers

modelos

Todos los modelos
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...

Contexto
1K
Entrada
text
Salida
embeddings
Entrada: $0.0050Salida: Gratispor millón de tokens
Ver detalles del modelo →
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...

Contexto
1K
Entrada
text
Salida
embeddings
Entrada: $0.0050Salida: Gratispor millón de tokens
Ver detalles del modelo →
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.

Contexto
1K
Entrada
text
Salida
embeddings
Entrada: $0.0050Salida: Gratispor millón de tokens
Ver detalles del modelo →
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,...

Contexto
1K
Entrada
text
Salida
embeddings
Entrada: $0.0050Salida: Gratispor millón de tokens
Ver detalles del modelo →