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e5-v

royokong

We propose a framework, called E5-V, to adpat MLLMs for achieving multimodal embeddings. E5-V effectively bridges the modality gap between different types of inputs, demonstrating strong performance in multimodal embeddings even without fine-tuning. We also propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs, demonstrating better performance than multimodal training. The model uses a custom chat template that automatically wraps text inputs with the instruction "Summary above sentence in one word:" and image inputs with "Summary above image in one word:".

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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e5-v

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