PolDense-400M

モデル紹介

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.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

よくある質問

出典と追跡記録

许可证：gemma。模型介绍与第三方评测均以原来源的具体版本和方法为准。