[KennethEnevoldsen]

closes embeddings-benchmark/mteb#5714

Checklist

• My model has a model sheet, report, or similar
• My model has a reference implementation in mteb/models/model_implementations/, 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 are 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 trained on the evaluation dataset including training splits. If I have, I have disclosed it clearly.

[github-actions]

Model Results Comparison

Reference models: intfloat/multilingual-e5-large, google/gemini-embedding-001
New models evaluated: athrael-soju/BiUltraVBERT

Results for athrael-soju/BiUltraVBERT

task_name	athrael-soju/BiUltraVBERT	Max result	Model with max result	In Training Data
Vidore3NuclearRetrieval	.213	.566	tencent/EVIE-8B	False
Vidore3NuclearRetrieval.v2	.248	.551	perplexity-ai/pplx-embed-v2-late-9b	False
Vidore3TelecomRetrieval	.421	.743	tencent/EVIE-8B	False
Vidore3TelecomRetrieval.v2	.470	.733	webAI-Official/webAI-ColVec1.1-8b	False
Average	.338	.648	nan	-

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