Mursit-Large-TR-Retrieval is a large-scale Turkish embedding model pre-trained entirely from scratch on Turkish-dominant corpora and fine-tuned for retrieval tasks. The model is based on ModernBERT-large architecture (403M parameters) and optimized specifically for Turkish legal domain applications. This model achieves strong performance on Turkish retrieval benchmarks with 56.87 MTEB Score and 46.56 Legal Score, ranking among the top Turkish embedding models. **Key Features:** - Pre-trained from scratch on approximately 112.7 billion tokens of Turkish-dominant corpus - Post-trained for embedding tasks using contrastive learning on MS MARCO-TR dataset - Achieves strong performance on Turkish legal retrieval benchmarks - Optimized for Turkish legal domain with custom tokenizer trained on legal documents **Model Type:** Embedding **Parameters:** 403M **Base Model:** newmindai/Mursit-Large **Architecture:** ModernBERT-large **Embedding Dimension:** 1,024 **Max Sequence Length:** 2,048 tokens

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