PolDense is a family of Polish embedding models optimized for dense retrieval. The models are built on top of the ettin-encoders, which use the ModernBERT architecture, and are designed to provide high-quality text representations for Polish search, retrieval, and RAG applications. The family consists of six models, ranging from 17M to 1B parameters. This makes it possible to choose between lightweight, low-latency deployments and larger models aimed at maximum retrieval quality. PolDense models were trained in a three-stage pipeline with two knowledge distillation stages and one fine-tuning stage. The main teacher embedding model was BGE-Multilingual-Gemma2, while the final retrieval fine-tuning stage used relevance scores produced by BGE-Reranker-v2.5-Gemma2-Lightweight. 1. The first stage adapted the original English ettin-encoder models to Polish using multilingual knowledge distillation. This stage follows the approach introduced in Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. The core idea is to use a strong teacher model to define a shared semantic embedding space. For each parallel sentence pair, the teacher encodes the source-language sentence, and the student is trained to produce a similar embedding for the translated sentence. In practice, this teaches the student to map Polish and English texts with the same meaning to nearby locations in the vector space, while preserving the retrieval-oriented structure learned by the teacher. In this stage, BGE-Multilingual-Gemma2 was used as the teacher model. Training was performed on a Polish-English parallel corpus containing approximately 20 million text pairs. Each model was trained for 5 epochs with a batch size of 64.

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