The embedding model trained by Jina AI . jina embeddings v3: Multilingual Embeddings With Task LoRA Quick Start Blog Azure AWS SageMaker API Intended Usage & Model Info jina embeddings v3 is a multilingual multi task text embedding model designed for a variety of NLP applications. Based on the Jina XLM RoBERTa architecture, this model supports Rotary Position Embeddings to handle long input sequences up to 8192 tokens . Additionally, it features 5 LoRA adapters to generate task specific embeddings efficiently. Key Features: Extended Sequence Length: Supports up to 8192 tokens with RoPE. Task Specific Embedding: Customize embeddings through the task argument with the following options: retrieval.query : Used for query embeddings in asymmetric retrieval tasks retrieval.passage : Used for passage embeddings in asymmetric retrieval tasks separation : Used for embeddings in clustering and re ranking applications classification : Used for embeddings in classification tasks text matching : Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks Matryoshka Embeddings : Supports flexible embedding sizes ( 32, 64, 128, 256, 512, 768,…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy