update reference

README.md

@@ -1137,6 +1137,22 @@ some useful tricks:
 2. Dataset sampler, we use M3E's dataset sampler to ensure that the samples in each batch come from a dataset, and negative samples are more valuable.
 3. instruction. Instruction has greatly improved the performance of the retrieval task in our experiments. We added instructions like 'query: ' and 'result: ' before each training sample.
 
+## Reference
+
+这里我们列出了我们参考过的embedding项目和论文
+1. [M3E](https://github.com/wangyuxinwhy/uniem)。非常棒的中文开源embedding项目，收集和整理了较多的中文高质量数据集，uniem也是一个不错的框架。
+2. [Text2vec](https://github.com/shibing624/text2vec)。另一个一个非常棒的中文开源embedding项目。
+3. [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)。智源AI开源的embedding模型，收集和整理了CMTEB benchmark，填补了中文embedding系统性评测的空缺。
+4. [E5](https://github.com/microsoft/unilm/tree/master/e5)。来自微软的一篇文章，有非常详细的消融实验以及数据处理过滤细节。
+5. [GTE](https://arxiv.org/abs/2308.03281)。一篇来自阿里达摩的embedding论文。
+
+Here we list the embedding projects and papers we have referenced
+1. [M3E](https://github.com/wangyuxinwhy/uniem). A great Chinese open source embedding project that collects and organizes a large number of high-quality Chinese datasets. Uniem is also a good framework.
+2. [Text2vec](https://github.com/shibing624/text2vec). Another great Chinese open source embedding project.
+3. [Flag Embedding](https://github.com/FlagOpen/FlagEmbedding). Zhiyuan AI’s open source embedding model.They collect and organize CMTEB benchmark, filling the gap in systematic evaluation of Chinese embeddings.
+4. [E5](https://github.com/microsoft/unilm/tree/master/e5). Powerd by microsoft，producing very detailed ablation experiments and data processing filtering details.
+5. [GTE](https://arxiv.org/abs/2308.03281). An embedding paper from Alibaba Damo.
+
 
 ## License
 Piccolo 使用 MIT License，免费商用。