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.
https://api.deepinfra.com/v1/openai/v1/embeddings| Model | Context | Max out |
|---|---|---|
| all-MiniLM-L12-v2 | — | — |
| all-MiniLM-L6-v2 | — | — |
| all-mpnet-base-v2 | — | — |
| clip-ViT-B-32-multilingual-v1 | — | — |
| clip-ViT-B-32 | — | — |
| multi-qa-mpnet-base-dot-v1 | — | — |
| paraphrase-MiniLM-L6-v2 | — | — |
/v1/models/deepinfra/sentence-transformers/multi-qa-mpnet-base-dot-v1We 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.