1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本 2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据 1. 对比学习损失函数 2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例) 3. EWC(Elastic Weights Consolidation)[4] 4. cosent loss[5] 5. 每一种类型的数据一个迭代器，分别计算loss进行更新 stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction( 比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。

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