
MTEB for comparing embedding models across tasks
Evaluate embeddings on documented retrieval, classification and other benchmark tasks.
Explore AI projects, understand what they do and find something you can build with.
Showing 109–114 of 224 projects · Page 19 of 38

Evaluate embeddings on documented retrieval, classification and other benchmark tasks.

Generate embeddings in an application using supported ONNX-backed models.

Evaluate and use embedding and reranking models from the BGE family.

Expose supported embedding and reranking models through a dedicated inference service.

Encode inputs for semantic search or score query-document pairs with a cross-encoder.

Work with ColBERT-style representations and retrieval in a Python library.
Each page introduces the project, explains its capabilities and highlights practical takeaways. Follow the repository link for the latest documentation and setup instructions.
Sign in, choose Add Repo and paste the main URL of a public AI repository, not a file or issue link. Franklin AI uses the project’s README to build and publish a page and links your contribution to your profile. Each account can add up to 20 repositories a day.
Private repositories cannot be added. If the README is too short for an automatic page, or the project is already being reviewed, the repository is saved in your submissions with the reason for an admin to complete, and you are credited when it is published.
Its existing page stays as it is. Adding it again links that page to your profile instead of creating a second one.
Use the Category filter. Each card shows its categories, such as AI Agents or AI Development, and the repository’s owner and name.