Hey, these are the scores for the JinaVDR benchmark.

Our aim with the JinaVDR (Visual Document Retrieval) benchmark is to expand upon the work of these prior benchmarks by incorporating visually rich multilingual documents with complex layouts like graphs, charts, and tables (mixed with text and images), as well as adding real-world queries and questions.

We included the benchmark results of four models:

• vidore/colpali-v1.2
• vidore/colpali-v1.3
• vidore/colqwen2.5-v0.2
• jinaai/jina-embeddings-v4

The benchmarks were each run on an H100 GPU.

Please find the benchmark PR here: embeddings-benchmark/mteb#2942

Checklist

• My model has a model sheet, report or similar
• My model has a reference implementation in mteb/models/ this can be as an API. Instruction on how to add a model can be found here
  • No, but there is an existing PR ___
• The results submitted is obtained using the reference implementation
• My model is available, either as a publicly accessible API or publicly on e.g., Huggingface
• I solemnly swear that for all results submitted I have not on the evaluation dataset including training splits. If I have I have disclosed it clearly.