Cutting-edge vision-language model exceling in retrieving text and metadata from images.
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Powerful OCR model for fast, accurate real-world image text extraction, layout, and structure analysis.
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Powerful OCR model for fast, accurate real-world image text extraction, layout, and structure analysis.
GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.
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.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Cutting-edge vision-language model exceling in retrieving text and metadata from images.
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.
Context-aware chart extraction that can detect 18 classes for chart basic elements, excluding plot elements.
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Multilingual text reranking model.
English text embedding model for question-answering retrieval.
Multilingual text question-answering retrieval, transforming textual information into dense vector representations.
Optimized community model for text embedding.
