Model overview
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
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。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。