MODEL PROFILE

marqo-ecommerce-embeddings-B

Marqo · marqo-ecommerce-embeddings-B

In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset. **Released Content**: 1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models 2) GoogleShopping-1m and AmazonProducts-3m for evaluation 3) Evaluation Code

multimodal retrievallicense:apache-2.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:50
OVERVIEW

Model overview

In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. The benchmarking results show that the Marqo-Ecommerce models consistently outperformed *all other models* across various metrics. Specifically, `marqo-ecommerce-L` achieved an average improvement of **17.6% in MRR** and **20.5% in nDCG@10** when compared with the current best open source model, `ViT-SO400M-14-SigLIP` across all three tasks in the `marqo-ecommerce-hard` dataset. When compared with the best private model, `Amazon-Titan-Multimodal`, we saw an average improvement of **38.9% in MRR** and **45.1% in nDCG@10** across all three tasks, and **35.9% in Recall** across the Text-to-Image tasks in the `marqo-ecommerce-hard` dataset. **Released Content**: 1) Marqo-Ecommerce-B and Marqo-Ecommerce-L embedding models 2) GoogleShopping-1m and AmazonProducts-3m for evaluation 3) Evaluation Code

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Are these real-time billing prices?

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。