NVIDIA Nemotron Parse v1.2 EU AI Act Compliance Profile

Model Overview

Description:

NVIDIA Nemotron Parse v1.2 is designed to understand document semantics and extract text and tables elements with spatial grounding. Given an image, NVIDIA Nemotron Parse v1.2 produces structured annotations, including formatted text, bounding-boxes and the corresponding semantic classes, ordered according to the document's reading flow. It overcomes the shortcomings of traditional OCR technologies that struggle with complex document layouts with structural variability, and helps transform unstructured documents into actionable and machine-usable representations. This has several downstream benefits such as increasing the availability of training-data for Large Language Models (LLMs), improving the accuracy of extractor, curator, retriever and AI agentic applications, and enhancing document understanding pipelines.<br>

This model is ready for commercial use. <br>

Release Notes

Compared to NVIDIA-Nemotron-Parse v1.1 the following changes are made in v1.2:

Updated prompt interface

NVIDIA-Nemotron-Parse v1.2 extends the prompt interface with a fourth prompt token category — Text-in-picture prompts — controlling whether the model extracts text from embedded images (e.g. photographs, figures):

• <predict_text_in_pic>: extract text found inside pictures.
• <predict_no_text_in_pic>: skip text extraction from pictures, reducing generation time by avoiding unnecessary tokens.

The full v1.2 prompt now consists of four prompt tokens (one from each category):

</s><s><predict_bbox><predict_classes><output_markdown><predict_no_text_in_pic>

Important: NVIDIA-Nemotron-Parse v1.1 used a three-token prompt (e.g. </s><s><predict_bbox><predict_classes><output_markdown>). In v1.2, the fourth token is required. The prompt is not validated at runtime — the model will still generate output with the old three-token prompt — but because all v1.2 training data included four prompt tokens, generation quality will be significa

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