nateraw / bge-large-en-v1.5

BAAI's bge-en-large-v1.5 for embedding text sequences

  • Public
  • 191.6K runs
  • GitHub
  • Paper

Input

Output

Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 85 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Embeddings are a powerful tool for working with text. By “embedding” text into vectors, you encode its meaning into a representation that can more easily be used for tasks like semantic search, clustering, and classification. To learn more, check out our guide on embeddings.

bge-large-en-v1.5 is a state-of-the-art open source model for text embeddings. It is ranked higher than OpenAI embeddings on the MTEB leaderboard, and is 4x cheaper to run on Replicate for large-scale text embedding.

The “BAAI General Embedding” (BGE) suite of models, released by the Beijing Academy of Artificial Intelligence (BAAI), are open source and available on the Hugging Face Hub.

Find out more about this model here: https://huggingface.co/BAAI/bge-large-en-v1.5