TurkEmbed4Retrieval: Turkish Embedding Model for Retrieval Task

In this work, we introduce TurkEmbed4Retrieval, a retrieval-specialized variant of the TurkEmbed model originally designed for Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. By fine-tuning the base model on the MS-MARCO-TR dataset using advanced training techniques, including Matryoshka representation learning and a tailored multiple negatives ranking loss, we achieve state-of-the-art (SOTA) performance for Turkish retrieval tasks. Extensive experiments demonstrate that our model outperforms Turkish-colBERT by 19–26% on key retrieval metrics for the Scifact-TR dataset, thereby establishing a new benchmark for Turkish information retrieval.