GLuCoSE v2 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. Model Description The model is based on GLuCoSE and fine tuned through distillation using several large scale embedding models and multi stage contrastive learning. Maximum Sequence Length: 512 tokens Output Dimensionality: 768 tokens Similarity Function: Cosine Similarity Usage Direct Usage (Sentence Transformers) You can perform inference using SentenceTransformer with the following code: Direct Usage (Transformers) You can perform inference using Transformers with the following code: Training Details The fine tuning of GLuCoSE v2 is carried out through the following steps: Step 1: Ensemble distillation The embedded re…
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