Get embeddings
These models generate vector representations that capture the semantics of text, images, and more. Embeddings power search, recommendations, and clustering.
Our pick for text: Multilingual E5
For most text applications, we recommend beautyyuyanli/multilingual-e5-large. It’s fast, cheap and produces high-quality embeddings suitable for semantic search, topic modeling, and classification.
Our pick for images: CLIP
CLIP is the go-to model for image similarity search and clustering. Incredibly popular and cost-effective, CLIP embeddings capture the semantic content of images, making it easy to find similar ones. Just pass in an image URL or a text string and you’re good to go.
Best for multimodal: ImageBind
To jointly embed text, images, and audio, ImageBind is in a class of its own. While more expensive than unimodal models, its ability to unify different data types enables unique applications like searching images with text queries or finding relevant audio clips. If you’re working on multimodal search or retrieval, ImageBind is worth the investment.
Featured models
Recommended models
zsxkib / jina-clip-v2
Jina-CLIP v2: 0.9B multimodal embedding model with 89-language multilingual support, 512x512 image resolution, and Matryoshka representations
cuuupid / gte-qwen2-7b-instruct
Embed text with Qwen2-7b-Instruct
lucataco / snowflake-arctic-embed-l
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance
adirik / e5-mistral-7b-instruct
E5-mistral-7b-instruct language embedding model
lucataco / nomic-embed-text-v1
nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks
nateraw / bge-large-en-v1.5
BAAI's bge-en-large-v1.5 for embedding text sequences
andreasjansson / llama-2-13b-embeddings
Llama2 13B with embedding output
mark3labs / embeddings-gte-base
General Text Embeddings (GTE) model.
replicate / all-mpnet-base-v2
This is a language model that can be used to obtain document embeddings suitable for downstream tasks like semantic search and clustering.