Summary
The video introduces a novel embedding model that is multimodal and multilingual, catering to text and code retrieval tasks. It discusses the limitations of traditional text and image embeddings in terms of information loss and storage issues. The evolution to chunk-level embeddings from token-level embeddings is explored, along with the challenges of storage and scalability. The Nvidia multimodal rag model and the Nemo Retriever model are presented, detailing their architectures, support for various tasks, and the generation of embeddings with different vector sizes. The embedding model's capabilities include processing high-resolution images, supporting single and multi-vector representations, and adjusting vector sizes for efficiency, with details provided on model weight availability, language support, and task-specific applications.
Introduction to Multimodal Embedding Model
Introducing a new embedding model that is multimodal and multilingual, suitable for text and code retrieval tasks. The model's weights are available on Hugging Face.
Traditional Text and Image Representation
Discussion on the traditional methods of converting text and images to embeddings, highlighting the loss of information and storage concerns.
Evolution of Embedding Models
Exploration of the evolution from token-level embeddings to chunk-level embeddings and the challenges with storage and scalability.
Nvidia Multimodal Rag Model
Overview of the Nvidia multimodal rag model, its architecture, support for text and image retrieval, and the usage of the same embedding space.
Nemo Retriever Model
Introduction to the Nemo Retriever model, its architectural details, support for various tasks, and the generation of embeddings for different vector sizes.
Embedding Model Capabilities
Explanation of the embedding model's capabilities, including processing high-resolution images, support for single and multi-vector representations, and adjusting vector sizes for cost and efficiency.
Model Weights and Language Support
Details on the availability of model weights, languages supported, and the use of the same model for generating embeddings.
Task Specific Applications
Discussion on task-specific applications of the embedding model, such as text matching, code retrieval, and utilizing multi-vector representations.
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