ColQwen3.5-v1 Achieves SOTA on ViDoRe V1
The ColQwen3.5-v1 model, a 4.5 billion parameter model built on Qwen3.5-4B, has achieved the top ranking on ViDoRe V1 with an nDCG@5 score of 0.917. The model was trained using a late-interaction approach and includes phases of hard negative mining and domain specialization in finance and table documents. The model's weights are available on Hugging Face, and a pull request has been raised for merging improvements.
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The ColQwen3.5-v1 model, a 4.5 billion parameter model built on Qwen3.5-4B, has achieved the top ranking on ViDoRe V1 with an nDCG@5 score of 0.917. The model was trained using a late-interaction approach and includes phases of hard negative mining and domain specialization in finance and table documents. The model's weights are available on Hugging Face, and a pull request has been raised for merging improvements.
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