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World-class multilingual and cross-lingual question-answering retrieval.

Predicts the 3D structure of a protein from its amino acid sequence.

Generates embeddings of proteins from their amino acid sequences.

deepmind/alphafold2

Predicts the 3D structure of a protein from its amino acid sequence.

nvidia/nv-dinov2

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

ProteinMPNN is a deep learning model for predicting amino acid sequences for protein backbones.

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

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

nvidia/nvclip

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

Generates high-quality numerical embeddings from text inputs.

baai/bge-m3

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

A generative model of protein backbones for protein binder design.

snowflake/arctic-embed-l

GPU-accelerated generation of text embeddings.

nvidia/embed-qa-4

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

meta/esmfold

Predicts the 3D structure of a protein from its amino acid sequence.

Predicts the 3D structure of how a molecule interacts with a protein.