Multimodal question-answer retrieval representing user queries as text and documents as images.
The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
Generates a multiple sequence alignment from a query sequence and a protein sequence database search.
Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Predicts the 3D structure of a protein from its amino acid sequence.
Predicts the 3D structure of a protein from its amino acid sequence.
ProteinMPNN is a deep learning model for predicting amino acid sequences for protein backbones.
English text embedding model for question-answering retrieval.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
Generates high-quality numerical embeddings from text inputs.
A generative model of protein backbones for protein binder design.
Optimized community model for text embedding.
GPU-accelerated generation of text embeddings used for question-answering retrieval.