nateraw / bge-large-en-v1.5

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

  • Public
  • 293.4K runs
  • GitHub
  • Paper
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Playground API Examples README Versions

Run time and cost

This model costs approximately $0.062 to run on Replicate, or 16 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 64 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