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sentence-transformers

Sentence Transformers: all-mpnet-base-v2

Descrizione della fonte (inglese)

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...

Vedi la fonte su OpenRouter Hugging Face
Panoramica

Specifiche del modello

Contesto
512 tokens
Output massimo
460 tokens
Architettura
text->embeddings
Tokenizer
Other
Limite di conoscenza
Non indicato
Moderato
No
OPENROUTER

Prezzi completi

Tariffe sincronizzate da OpenRouter, per milione di token.

Input
$0.005
per milione di token
API

Avvio rapido

Imposta OPENROUTER_API_KEY localmente. Python richiede requests; JavaScript viene eseguito in Node.js. Conserva la chiave sul server.

Documentazione 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"}'
Capacità e modalità

Input → Output

Input
text
Output
embeddings
API

Parametri API supportati

Provider disponibili

1 Provider

Verificato: 16 settembre 2026

Provider live su OpenRouter

Disponibilità, latenza, throughput e routing cambiano continuamente. Consulta la fonte per i dati correnti.

OpenRouter

DeepInfra

Non indicato
Contesto
1K
Output massimo
0K
Input
$0.005
Output
Gratuito
Lettura cache
Non indicato
Scrittura cache
Non indicato
sentence-transformers

4 modelli

Tutti i modelli
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...

Contesto
1K
Input
text
Output
embeddings
Input: $0.005per milione di token
Vedi i dettagli del modello →
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...

Contesto
1K
Input
text
Output
embeddings
Input: $0.005per milione di token
Vedi i dettagli del modello →
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.

Contesto
1K
Input
text
Output
embeddings
Input: $0.005per milione di token
Vedi i dettagli del modello →
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,...

Contesto
1K
Input
text
Output
embeddings
Input: $0.005per milione di token
Vedi i dettagli del modello →