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

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
/M tokens
API

Avvio rapido

Chiama questo modello tramite l’API compatibile OpenAI di OpenRouter.

curl https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sentence-transformers/all-minilm-l12-v2","input":"Your text here"}'
Capacità e modalità

Input → Output

Input
text
Output
embeddings
API

Parametri API supportati

frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Provider disponibili

1 Provider

Provider live su OpenRouter

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

OpenRouter

DeepInfra

unknown
Contesto
1K
Output massimo
0K
Input
$0.0050
Output
Gratuito
Lettura cache
Non indicato
Scrittura cache
Non indicato
sentence-transformers

modelli

Tutti i modelli
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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: $0.0050Output: Gratuitoper 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: $0.0050Output: Gratuitoper milione di token
Vedi i dettagli del modello →
sentence-transformers logosentence-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...

Contesto
1K
Input
text
Output
embeddings
Input: $0.0050Output: Gratuitoper 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: $0.0050Output: Gratuitoper milione di token
Vedi i dettagli del modello →