NVIDIA-Nemotron-Parse-v1.2

模型介绍

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. 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): > **Important:** NVIDIA-Nemotron-Parse v1.1 used a three-token prompt (e.g. ` `). 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 significantly degraded without it.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。