Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
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.
Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
Cutting-edge vision-language model exceling in high-quality reasoning from images.
Cutting-edge vision-Language model exceling in high-quality reasoning from images.
English text embedding model for question-answering retrieval.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
GPU-accelerated generation of text embeddings used for question-answering retrieval.
GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.
