Model Overview Description: The llama nemotron rerank vl 1b v2 was developed by NVIDIA for multimodal question answering retrieval. It is optimized for providing a logit score that represents how relevant a document page is to a given query. The model can process documents in the form of image, text, or image and text combined. The expected images are screenshots of document pages or slides. Documents are ranked given a user query in text form. The model supports images containing text, tables, charts, and infographics. We report the model's performance by evaluating it on the popular ViDoRe V1, V2 and the new Vidore V3 (see Vidore LB for details) multimodal retrieval benchmarks, and on two internally curated visual retrieval datasets. The reranking model serves as a key component of a multimodal retrieval system, such as a vision RAG pipeline, where it helps improve overall accuracy. A multimodal retrieval system often uses a multimodal embedding model (dense) to return relevant documents given the input. A reranking model can be used to rerank the potential candidates into a final order. The reranking model takes the query and document pairs as input, and its self attention can p…
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