PLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, Inc. It can convert Japanese text input into numerical vectors and can be used for a wide range of applications, including information retrieval, text classification, and clustering.

LLaMA 1.0 (13B) model fine-tuned for academic use cases like text classification. Use at your own risk. Not to be confused with the vision model.

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

The Qwen3 Embedding 8B model is the latest proprietary model of the Qwen family, specifically designed for text embedding tasks. This model inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills building upon the dense foundational models of the Qwen3 series. The model represents significant advancements in multiple text embedding tasks including text retrieval, code retrieval, text classification, text clustering.

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining

significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining