TurkEmbed: Turkish Embedding Model on NLI & STS Tasks

• URL: http://arxiv.org/abs/2511.08376v1
• Date: Wed, 12 Nov 2025 01:56:21 GMT
• Title: TurkEmbed: Turkish Embedding Model on NLI & STS Tasks
• Authors: Özay Ezerceli, Gizem Gümüşçekiçci, Tuğba Erkoç, Berke Özenç,
• Abstract summary: TurkEmbed is a novel Turkish language embedding model designed to outperform existing models.<n>It utilizes a combination of diverse datasets and advanced training techniques, including matryoshka representation learning.<n>It surpasses the current state-of-the-art model, Emrecan, on All-NLI-TR and STS-b-TR benchmarks, achieving a 1-4% improvement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces TurkEmbed, a novel Turkish language embedding model designed to outperform existing models, particularly in Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. Current Turkish embedding models often rely on machine-translated datasets, potentially limiting their accuracy and semantic understanding. TurkEmbed utilizes a combination of diverse datasets and advanced training techniques, including matryoshka representation learning, to achieve more robust and accurate embeddings. This approach enables the model to adapt to various resource-constrained environments, offering faster encoding capabilities. Our evaluation on the Turkish STS-b-TR dataset, using Pearson and Spearman correlation metrics, demonstrates significant improvements in semantic similarity tasks. Furthermore, TurkEmbed surpasses the current state-of-the-art model, Emrecan, on All-NLI-TR and STS-b-TR benchmarks, achieving a 1-4\% improvement. TurkEmbed promises to enhance the Turkish NLP ecosystem by providing a more nuanced understanding of language and facilitating advancements in downstream applications.

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