Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
Authors: name: Junqin Huang, name: Zhongjie Hu, name: Zihao Jing, name: Mengya Gao, name: Yichao Wu

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/