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The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.

Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.

Generates embeddings of proteins from their amino acid sequences.

NV-DINOv2 is a visual foundation model that generates vector embeddings for the input image.

English text embedding model for question-answering retrieval.

Multilingual text question-answering retrieval, transforming textual information into dense vector representations.

NV-CLIP is a multimodal embeddings model for image and text.

Generates high-quality numerical embeddings from text inputs.

Embedding model for text retrieval tasks, excelling in dense, multi-vector, and sparse retrieval.

Optimized community model for text embedding.

GPU-accelerated generation of text embeddings used for question-answering retrieval.