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sentence-transformers

Sentence Transformers: all-mpnet-base-v2

Descripción de la fuente (inglés)

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...

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
por millón de tokens
API

Inicio rápido

Configura OPENROUTER_API_KEY localmente. Python requiere requests; JavaScript se ejecuta en Node.js. Mantén la clave en el servidor.

Documentación de la API

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"}'
Capacidades y modalidades

Entrada → Salida

Entrada
text
Salida
embeddings
API

Parámetros API compatibles

Proveedores disponibles

1 Proveedor

Verificado: 16 de septiembre de 2026

Proveedores en vivo en OpenRouter

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

OpenRouter

DeepInfra

No indicado
Contexto
1K
Salida máxima
0K
Entrada
$0.005
Salida
Gratis
Lectura de caché
No indicado
Escritura de caché
No indicado
sentence-transformers

4 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: $0.005por 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: $0.005por 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: $0.005por 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: $0.005por millón de tokens
Ver detalles del modelo →