## 2 Related Work  
Traditional sentence vector standard-type algorithms perform post-processing on top of BERT-generated sentence vectors without directly optimising for the BERT model itself, and these methods usually neglect how to make the BERT model itself generate better sentence vectors. However, due to the feature space collapse problem in BERT models,researchers have started to focus on contrast learning methods to solve the feature space collapse problem [3]. Contrastive learning aims to distance the vectors between dissimilar samples while bringing the vectors between similar samples closer together to learn a higher-quality representation [4]. The method forces the model to cluster similar samples in the embedding space by introducing a similarity metric into the training process in order to better learn a more discriminative feature representation. Methods that use contrast learning to solve the feature space collapse problem have made significant progress in improving the quality of sentence representations compared with traditional standard-type algorithms for sentence vectors. These approaches have enabled models to focus more on similarity and differentiation when generating sentence vectors, thus enabling better application to a variety of natural language processing tasks. [PERSON] et al. [3] proposed a contrast learning framework, ConSERT, using BERT as a shared encoder with the last layer output averaged and pooled as a sentence vector, and proposed data enhancement strategies such as Dropout, constructing adversarial samples based on gradients, randomly cropping words or features, and disrupting word order to construct positive and negative samples for contrast learning. Experimentally, ConSERT proved to be effective in ameliorating the problem of collapsed representations of native BERT sentences. [PERSON] et al. [2] proposed a simple contrast learning framework, SimCSE, with two training approaches: an unsupervised approach, where two different representations of the same sentence are obtained as positive sample pairs through the Dropout mechanism and constitute negative sample pairs with other sentences in the same batch; and a supervised approach, where using the NLI dataset, sentence pairs with embedded relations are used as positive sample pairs and sentence pairs with contradictory relations are used as negative sample pairs for comparison training. The ESimCSE proposed by [PERSON] et al. in 2022 [5] addresses two problems: (1) positive examples constructed using Dropout have the same sentence length, leading the model to consider sentences of the same length as more similar; and (2) increasing the batch size introduces more negative examples, which in turn leads to a decrease in effectiveness. To address these two issues, ESimCSE uses repeated words to construct positive examples and momentum sequences to expand negative samples. The ArcCSE proposed by [PERSON] et al. [6] addresses two problems: (1) vulnerability to noisy data interference and (2) inability to model the semantic order between multiple sentences. To address these issues, ArcCSE converts the NT-Xent objective function, which previously operated in Euclidean space, to angle space and adds angle decision boundaries, resulting in a more robust sentence representation. In addition, to model the semantic order between multiple sentences, ArcCSE proposes a new pre-training task to model the implication relations of ternary sentence pairs. [PERSON] et al. [7] proposed Sentence-T5 (abbreviated as ST5) to train a sentence vector model by contrast learning using CommQA and NLI datasets with a twin-tower structure. Experiments show that the average pooling result of the ST5 encoder output works best as a sentence vector. [PERSON] et al. [8] proposed a joint self-contrast learning and feature de-correlation method, SCD. On the semantic similarity task, SCD does not have an advantage over SimCSE but has a respectable performance on several other downstream tasks. [PERSON] et al.