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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.06932 |
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| _version_ | 1866909200056057856 |
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| author | Huang, Junqin Hu, Zhongjie Jing, Zihao Gao, Mengya Wu, Yichao |
| author_facet | Huang, Junqin Hu, Zhongjie Jing, Zihao Gao, Mengya Wu, Yichao |
| contents | In this report, we introduce Piccolo2, an embedding model that surpasses other models in the comprehensive evaluation over 6 tasks on CMTEB benchmark, setting a new state-of-the-art. Piccolo2 primarily leverages an efficient multi-task hybrid loss training approach, effectively harnessing textual data and labels from diverse downstream tasks. In addition, Piccolo2 scales up the embedding dimension and uses MRL training to support more flexible vector dimensions. The latest information of piccolo models can be accessed via: https://huggingface.co/sensenova/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06932 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training Huang, Junqin Hu, Zhongjie Jing, Zihao Gao, Mengya Wu, Yichao Computation and Language Artificial Intelligence In this report, we introduce Piccolo2, an embedding model that surpasses other models in the comprehensive evaluation over 6 tasks on CMTEB benchmark, setting a new state-of-the-art. Piccolo2 primarily leverages an efficient multi-task hybrid loss training approach, effectively harnessing textual data and labels from diverse downstream tasks. In addition, Piccolo2 scales up the embedding dimension and uses MRL training to support more flexible vector dimensions. The latest information of piccolo models can be accessed via: https://huggingface.co/sensenova/ |
| title | Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.06932 |