  Sentence encoder encode the semantics of their input, enabling key downstream
applications such as classification, clustering, or retrieval. In this paper,
we present Serafim PT*, a family of open-source sentence encoders for
Portuguese with various sizes, suited to different hardware/compute budgets.
Each model exhibits state-of-the-art performance and is made openly available
under a permissive license, allowing its use for both commercial and research
purposes. Besides the sentence encoders, this paper contributes a systematic
study and lessons learned concerning the selection criteria of learning
objectives and parameters that support top-performing encoders.
