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University at Buffalo
cached
Context-aware chart extraction that can detect 18 classes for chart basic elements, excluding plot elements.
Model
nemo retriever
+2
738
1y
NVIDIA
llama-3.2-nemoretriever-1b-vlm-embed-v1
Multimodal question-answer retrieval representing user queries as text and documents as images.
Model
nemo retriever
+3
272K
8mo
NVIDIA
llama-3.2-nemoretriever-500m-rerank-v2
GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.
Model
nemo retriever
+2
1.08K
8mo
NVIDIA
llama-3.2-nv-embedqa-1b-v2
Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.
Model
nemo retriever
+3
6.68M
7mo
NVIDIA
llama-3.2-nv-rerankqa-1b-v2
Fine-tuned reranking model for multilingual, cross-lingual text question-answering retrieval, with long context support.
Model
nemo retriever
+2
156K
7mo
NVIDIA
llama-3_2-nemoretriever-300m-embed-v1
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Model
Retrieval Augmented Generation
+2
58.09K
7mo
NVIDIA
llama-3_2-nemoretriever-300m-embed-v2
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Model
Retrieval Augmented Generation
+2
151K
5mo
NVIDIA
llama-nemotron-embed-1b-v2
Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.
Model
Retrieval Augmented Generation
+2
67.61K
2d
NVIDIA
llama-nemotron-embed-vl-1b-v2
Multimodal question-answer retrieval representing user queries as text and documents as images.
Model
nemo retriever
+3
627K
3w
NVIDIA
llama-nemotron-rerank-1b-v2
GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.
Model
nemo retriever
+2
Today
NVIDIA
nemoretriever-graphic-elements-v1
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+4
76.94K
11mo
NVIDIA
nemoretriever-ocr
Powerful OCR model for fast, accurate real-world image text extraction, layout, and structure analysis.
Model
Optical Character Recognition
+4
70.61K
7mo
NVIDIA
nemoretriever-ocr-v1
Powerful OCR model for fast, accurate real-world image text extraction, layout, and structure analysis.
Model
Optical Character Recognition
+4
556K
7mo
NVIDIA
nemoretriever-page-elements-v2
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+5
276K
11mo
NVIDIA
nemoretriever-page-elements-v3
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+4
690K
2mo
NVIDIA
nemoretriever-parse
Cutting-edge vision-language model exceling in retrieving text and metadata from images.
Model
optical character recognition
+4
295K
9mo
NVIDIA
nemoretriever-table-structure-v1
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+4
133K
11mo
NVIDIA
nemotron-graphic-elements-v1
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+4
3.53K
2d
NVIDIA
nemotron-table-structure-v1
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+4
2.75K
2d
NVIDIA
nv-embedcode-7b-v1
The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.
Model
nemo retriever
+2
323K
9mo
NVIDIA
nv-embedqa-e5-v5
English text embedding model for question-answering retrieval.
Model
Embedding
+4
3.22M
7mo
NVIDIA
nv-yolox-page-elements-v1
Model for object detection, fine-tuned to detect charts, tables, and titles in documents.
Model
Object Detection
+6
15.86K
8mo
Baidu
paddleocr
Model for table extraction that receives an image as input, runs OCR on the image, and returns the text within the image and its bounding boxes.
Model
Optical Character Recognition
+6
187K
8mo
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