NVIDIA-Nemotron-Parse-v1.2

모델 소개

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.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

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。

자주 묻는 질문

출처 및 추적 기록

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。