Sentence embedding models play a key role in the field of Natural Language Processing. They can be exploited for the resolution of several tasks like sentence paraphrasing, sentence similarity, and sentence clustering. Fine- tuning pre-trained models for sentence embedding extraction is a common practice that allows it to reach state-of-the-art performance on downstream tasks. Nevertheless, this practice usually requires labeled data sets. This thesis project aims to overcome this issue by introducing a novel technique for the automatic creation of a target set for fine-tuning sentence embedding models for a specific downstream task. The technique is evaluated on three distinct tasks: sentence paraphrasing, sentence similarity, and sentence clustering.