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GLuCoSE (General LUke-based COntrastive Sentence Embedding, "glucose") is a Japanese text embedding model based on LUKE. In order to create a general-purpose, user-friendly Japanese text embedding model, GLuCoSE has been trained on a mix of web data and various datasets associated with natural language inference and search. This model is not only suitable for sentence vector similarity tasks but also for semantic search tasks. - Maximum token count: 512 - Output dimension: 768 - Pooling: mean pooling - Supported language: Japanese You can use this model easily with sentence-transformers. Since the loss function used during training is cosine similarity, we recommend using cosine similarity for downstream tasks.

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This model is a general Japanese text embedding model, excelling in retrieval tasks. It can run on CPU and is designed to measure semantic similarity between sentences, as well as to function as a retrieval system for searching passages based on queries. Key features: - Specialized for retrieval tasks, it demonstrates the highest performance among similar size models in MIRACL and other tasks . - Optimized for Japanese text processing - Can run on CPU During inference, the prefix "query: " or "passage: " is required. Please check the Usage section for details.

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