We present a sentence similarity model based on the Sentence Transformers architecture, which maps sentences to a 384-dimensional dense vector space. The model uses a pre-trained BERT encoder and applies mean pooling on top of the contextualized word embeddings to obtain sentence embeddings. We evaluate the model on the Sentence Embeddings Benchmark.
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/paraphrase-MiniLM-L6-v2