Sentence Transformers is officially joining Hugging Face, marking a new chapter in the evolution of one of the most influential open-source libraries in natural language processing. The transition comes after years of development and community-driven innovation led by the Ubiquitous Knowledge Processing (UKP) Lab at Technische Universität Darmstadt, under the supervision of Prof. Dr. Iryna Gurevych. Since its introduction in 2019 by Dr. Nils Reimers, Sentence Transformers—also known as Sentence-BERT or SBERT—has become a foundational tool for generating high-quality semantic embeddings. Designed to overcome the limitations of standard BERT embeddings at the sentence level, the library uses a Siamese network architecture to produce embeddings that capture nuanced meaning and enable efficient comparison via cosine similarity. Its impact spans a wide range of applications, including semantic search, text clustering, paraphrase detection, and information retrieval. Over the past five years, the library has grown into a thriving ecosystem. More than 16,000 models are now publicly available on the Hugging Face Hub, serving over a million unique users each month. The project’s success has been fueled by continuous contributions from researchers, developers, and practitioners worldwide. Tom Aarsen from Hugging Face has been actively maintaining the library since late 2023, introducing major upgrades such as Sentence Transformers v3.0 with modern training pipelines, Cross Encoder v4.0, and Sparse Encoder v5.0. With the official transition, Hugging Face will provide enhanced infrastructure, including robust continuous integration, automated testing, and streamlined deployment tools to ensure the library stays at the forefront of NLP advancements. Despite the move, Sentence Transformers remains fully open-source under the Apache 2.0 license. The project will continue to prioritize transparency, collaboration, and accessibility. The community is encouraged to contribute through code, model sharing, documentation, and feedback. The UKP Lab’s legacy in semantic representation learning, supported by grants from the German Research Foundation (DFG), the German Federal Ministry of Education and Research (BMBF), and the Hessen State Ministry for Higher Education, Research and the Arts (HMWK), has laid a strong foundation. Their pioneering work in representation learning, large language models, and information retrieval continues to inspire new generations of AI researchers. Hugging Face expresses deep gratitude to the UKP Lab, especially Dr. Nils Reimers and Prof. Dr. Iryna Gurevych, for their vision and commitment. The team also thanks the global community for their ongoing support and contributions. For those new to the library or looking to explore its capabilities, the Hugging Face Hub offers comprehensive documentation, tutorials, and pre-trained models to get started quickly. The future of Sentence Transformers is bright, with continued innovation and community collaboration driving progress in semantic AI.