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Sentence Transformers · Embedding model
multi-qa-mpnet-base-dot-v1
sentence-transformers/multi-qa-mpnet-base-dot-v1- Embedding
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.
Specifications
- Input
- text
- Output
- vector per input (float array; base64 on request)
- Batch
- up to 512 inputs per call
- Billing
- per input token
Use it
POST /api/v1/embeddings · MCP embed · full reference
curl https://omnirail.org/api/v1/embeddings \
-H "Authorization: Bearer $OMNIRAIL_KEY" -H "Content-Type: application/json" \
-d '{"model":"sentence-transformers/multi-qa-mpnet-base-dot-v1","input":["brass lever espresso machine","coffee grinder"]}'