This model is a multilingual version of the OpenAI CLIP-ViT-B32 model, which maps text and images to a common dense vector space. It includes a text embedding model that works for 50+ languages and an image encoder from CLIP. The model was trained using Multilingual Knowledge Distillation, where a multilingual DistilBERT model was trained as a student model to align the vector space of the original CLIP image encoder across many languages.
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/clip-ViT-B-32-multilingual-v1This model is a multilingual version of the OpenAI CLIP-ViT-B32 model, which maps text and images to a common dense vector space. It includes a text embedding model that works for 50+ languages and an image encoder from CLIP. The model was trained using Multilingual Knowledge Distillation, where a multilingual DistilBERT model was trained as a student model to align the vector space of the original CLIP image encoder across many languages.