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README.md

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 This is the HuggingFace repository of the paper named [MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding](https://arxiv.org/pdf/2511.12449).
 
-Following internal legal, privacy, and compliance review (aligned with China’s PIPL), we have released **the CoIN benchmark**, including original images, titles and category/attribute annotations. All personally identifiable information has been rigorously removed. The training set and model weights are undergoing final security clearance, with a commitment to full public release.
+Following internal legal, privacy, and compliance review (aligned with China’s PIPL), we have released **the MBE2.0 benchmark**, including original images, titles and category/attribute annotations. All personally identifiable information has been rigorously removed. The training set and model weights are undergoing final security clearance, with a commitment to full public release.
 
-The CoIN Benchmark is a co-augmented multimodal representation benchmark designed specifically for representation learning and evaluation in e-commerce scenarios.
+The MBE2.0 Benchmark is a co-augmented multimodal representation benchmark designed specifically for representation learning and evaluation in e-commerce scenarios.
 
 Our data originates from Taobao, one of the largest e-commerce platforms in China. By collecting and processing user interaction logs spanning from January 1, 2023, to June 30, 2025, we have constructed dedicated training and test sets.
 
-Following internal open-source approval procedures, we hereby release the complete test set of the CoIN Benchmark. Due to repository size limitations imposed by the anonymous submission system, we provide a representative subset of 10,000 samples. The full training set and associated model checkpoints are currently undergoing internal review. We commit to releasing the complete dataset and models.
+Following internal open-source approval procedures, we hereby release the complete test set of the MBE2.0 Benchmark. Due to repository size limitations imposed by the anonymous submission system, we provide a representative subset of 10,000 samples. The full training set and associated model checkpoints are currently undergoing internal review. We commit to releasing the complete dataset and models.
 
 **This benchmark is for academic research use only and is prohibited from use in any commercial setting.**