Nemotron-3.5-Parse

Model overview

Nemotron 3.5 Parse is a document image parser that produces structured, machine-readable text with semantic layout classes, bounding boxes, and reading order. It combines a C-RADIOv4-SO400M vision encoder with a 10-layer mBART decoder that consumes the visual sequence as a causal prefix. The release uses dynamic rectangular NaViT preprocessing, content-normalized coordinates, grouped-query self-attention (16 query heads and 8 key/value heads), a 72,505-token multilingual tokenizer, and chart-aware output through the ` ` token. It is intended for document understanding, information retrieval, data extraction, and multimodal data-curation workflows. ParseBench results use triple pass with region union on 2,078 unique pages. See pipeline details, reproduction instructions, and evaluation results. MOSCAR uses simple synthetic renderings of text in each language to test basic OCR capability. These results do not guarantee accuracy on complex scanned documents, handwriting, degraded images, or other real-world variations. Character bag F1 measures character overlap without considering character order.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

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。

Frequently asked questions

Sources & traceability

许可证：openmdw-1.1。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。