Arctic Embed

Arctic Embed offers high-performance, open-source embedding models optimized for retrieval tasks, suitable for various enterprise applications. It balances efficiency and accuracy, topping MTEB retrieval benchmarks.

intermediate30-60 minutes3 steps

The play

Model Selection
Choose the appropriate Arctic Embed model size based on your performance and resource constraints. Smaller models (22M parameters) offer faster inference, while larger models (334M parameters) provide higher accuracy.
Text Embedding Generation
Use the selected Arctic Embed model to generate embeddings for your text data. This involves loading the model and passing your text through it.
Similarity Search
Implement a similarity search using the generated embeddings. Common methods include cosine similarity or dot product. Use a vector database for efficient search at scale.

Starter code

Start by selecting the appropriate Arctic Embed model size for your application.  Begin with the 'xs' model for experimentation and scale up as needed.

Source

Articlehuggingface.co