- Input
- $0.005
- Output
- $0.00
sentence-transformersListed Mar 3, 2023
multi-qa-mpnet-base-dot-v1
We present a sentence transformation model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for semantic search tasks. The model is trained on 215 million question-answer…
Source description
We present a sentence transformation model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for semantic search tasks. The model is trained on 215 million question-answer pairs from various sources, including WikiAnswers, PAQ, Stack Exchange, MS MARCO, GOOAQ, Amazon QA, Yahoo Answers, Search QA, ELI5, and Natural Questions. Our model uses a contrastive learning objective.
Measured performance
All leaderboards ↗Benchmark results
No published results in this index yet
Scores appear here when an evaluation matches this exact model.
Access & cost
1 quotePrices across providers
Model specifications
- Developer
- sentence-transformers
- Modality
- Embedding
- Context
- 512 tokens
- Maximum output
- 460 tokens
- Weights
- Open weights
- Provider listed
- Mar 3, 2023
- First discovered
- Oct 16, 2026
Exact identities
- openrouter
sentence-transformers/multi-qa-mpnet-base-dot-v1 - deepinfra
sentence-transformers/multi-qa-mpnet-base-dot-v1
Sources
Updated Oct 17, 2026
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