gte multilingual base The gte multilingual base model is the latest in the GTE (General Text Embedding) family of models, featuring several key attributes: High Performance : Achieves state of the art (SOTA) results in multilingual retrieval tasks and multi task representation model evaluations when compared to models of similar size. Training Architecture : Trained using an encoder only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode only LLM architecture (e.g., gte qwen2 1.5b instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed. Long Context : Supports text lengths up to 8192 tokens. Multilingual Capability : Supports over 70 languages. Elastic Dense Embedding : Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency. Sparse Vectors : In addition to dense representations, it can also generate sparse vectors. Paper : mGTE: Generalized Long Context Text Representation and Reranking Models for Multilingual Text Retrieval Model Information Model Size: 305…
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