[KennethEnevoldsen]

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: BAAI/bge-reranker-v2-m3, Qwen/Qwen3-VL-Reranker-2B, intfloat/multilingual-e5-small, mteb/baseline-bm25s, mteb/baseline-no-reranking, mteb/baseline-random-cross-encoder, nvidia/Nemotron-3.5-Rerank-8B-BF16

Results for BAAI/bge-reranker-v2-m3

task_name	BAAI/bge-reranker-v2-m3	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.437	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.620	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.529	.644	nan	-

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Results for Qwen/Qwen3-VL-Reranker-2B

task_name	Qwen/Qwen3-VL-Reranker-2B	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.500	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.703	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.601	.644	nan	-

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Results for intfloat/multilingual-e5-small

task_name	intfloat/multilingual-e5-small	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.281	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.488	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.384	.644	nan	-

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Results for mteb/baseline-bm25s

task_name	mteb/baseline-bm25s	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.328	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.472	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.400	.644	nan	-

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Results for mteb/baseline-no-reranking

task_name	mteb/baseline-no-reranking	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.501	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.699	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.600	.644	nan	-

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Results for mteb/baseline-random-cross-encoder

task_name	mteb/baseline-random-cross-encoder	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.106	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.133	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.119	.644	nan	-

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Results for nvidia/Nemotron-3.5-Rerank-8B-BF16

task_name	nvidia/Nemotron-3.5-Rerank-8B-BF16	Max result	Model with max result	In Training Data
Vidore3NuclearNemotron35Embed8BReranking.v2	.567	.543	Qwen/Qwen3-VL-Reranker-8B	False
Vidore3TelecomNemotron35Embed8BReranking.v2	.762	.744	Qwen/Qwen3-VL-Reranker-8B	False
Average	.665	.644	nan	-

Model have high performance on these tasks: Vidore3TelecomNemotron35Embed8BReranking.v2,Vidore3NuclearNemotron35Embed8BReranking.v2

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