title: Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
summary: 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/
paper_authors._id: 6642ebce90218ea2ee737dc7
paper_authors.name: Junqin Huang
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paper_authors.fullname: HuangJunqin
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paper_authors.user: Jinkin
paper_authors._id: 6642ebce90218ea2ee737dc8
paper_authors.name: Zhongjie Hu
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paper_authors.fullname: Zhongjie Hu
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paper_authors.user: Hzj0913
paper_authors._id: 6642ebce90218ea2ee737dc9
paper_authors.name: Zihao Jing
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paper_authors.fullname: Zihao Jing
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paper_authors.user: selmisskilig
paper_authors._id: 6642ebce90218ea2ee737dca
paper_authors.name: Mengya Gao
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paper_authors.fullname: Gaomengya
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paper_authors.user: gaomengya
paper_authors._id: 6642ebce90218ea2ee737dcb
paper_authors.name: Yichao Wu
paper_authors.status: admin_assigned
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paper_authors._id: 64f0451b9e7770db74dec33e
paper_authors.avatarUrl: /avatars/31b49461ad8781bf7ffbea6e7004be46.svg
paper_authors.fullname: Yichao Wu
paper_authors.type: user
paper_authors.user: chaorenwu111
paper_title: Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
paper_summary: 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/
paper_ai_summary: Piccolo2, an embedding model, achieves new state-of-the-art performance across six tasks using a multi-task hybrid loss approach, scaled embedding dimensions, and MRL training.
paper_ai_keywords: embedding model multi-task hybrid loss MRL training