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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2510.20674 |
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| _version_ | 1866915572174815232 |
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| author | R, Rakshith Sharma, Shubham Khan, Mohammed Sameer Chopra, Ankush |
| author_facet | R, Rakshith Sharma, Shubham Khan, Mohammed Sameer Chopra, Ankush |
| contents | This study presents the multilingual e-commerce search system developed by the Tredence_AICOE team. The competition features two multilingual relevance tasks: Query-Category (QC) Relevance, which evaluates how well a user's search query aligns with a product category, and Query-Item (QI) Relevance, which measures the match between a multilingual search query and an individual product listing. To ensure full language coverage, we performed data augmentation by translating existing datasets into languages missing from the development set, enabling training across all target languages. We fine-tuned Gemma-3 12B and Qwen-2.5 14B model for both tasks using multiple strategies. The Gemma-3 12B (4-bit) model achieved the best QC performance using original and translated data, and the best QI performance using original, translated, and minority class data creation. These approaches secured 4th place on the final leaderboard, with an average F1-score of 0.8857 on the private test set. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyticup E-commerce Product Search Competition Technical Report from Team Tredence_AICOE R, Rakshith Sharma, Shubham Khan, Mohammed Sameer Chopra, Ankush Information Retrieval Computation and Language This study presents the multilingual e-commerce search system developed by the Tredence_AICOE team. The competition features two multilingual relevance tasks: Query-Category (QC) Relevance, which evaluates how well a user's search query aligns with a product category, and Query-Item (QI) Relevance, which measures the match between a multilingual search query and an individual product listing. To ensure full language coverage, we performed data augmentation by translating existing datasets into languages missing from the development set, enabling training across all target languages. We fine-tuned Gemma-3 12B and Qwen-2.5 14B model for both tasks using multiple strategies. The Gemma-3 12B (4-bit) model achieved the best QC performance using original and translated data, and the best QI performance using original, translated, and minority class data creation. These approaches secured 4th place on the final leaderboard, with an average F1-score of 0.8857 on the private test set. |
| title | Analyticup E-commerce Product Search Competition Technical Report from Team Tredence_AICOE |
| topic | Information Retrieval Computation and Language |
| url | https://arxiv.org/abs/2510.20674 |