Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
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/
6642ebce90218ea2ee737dc7
Junqin Huang
claimed_verified
2024-05-14T07:07:20.795Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/v6rEJybo42A251aXU2CaO.png
HuangJunqin
user
Jinkin
6642ebce90218ea2ee737dc8
Zhongjie Hu
admin_assigned
2024-05-14T07:50:04.956Z
65ded6e6beffeb39ba6a91b9
/avatars/227ba16ea22768bcc334d01cf01522a0.svg
Zhongjie Hu
user
Hzj0913
6642ebce90218ea2ee737dc9
Zihao Jing
admin_assigned
2024-05-14T07:50:12.373Z
658e74ebf5a209eeacbbee9c
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/o99DsiqAav4Reg_6ymvkp.jpeg
Zihao Jing
user
selmisskilig
6642ebce90218ea2ee737dca
Mengya Gao
admin_assigned
2024-05-14T07:50:27.445Z
64f04220c26b0228ec5a20be
/avatars/0953a9bc7e45be7711382e58d7d3d515.svg
Gaomengya
user
gaomengya
6642ebce90218ea2ee737dcb
Yichao Wu
admin_assigned
2024-05-14T07:50:34.463Z
64f0451b9e7770db74dec33e
/avatars/31b49461ad8781bf7ffbea6e7004be46.svg
Yichao Wu
user
chaorenwu111
Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
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/
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.
embedding model multi-task hybrid loss MRL training