2 citations · 2 across the 4 of their papers we have counts for
5 papers
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
AICC: Parse HTML Finer, Make Models Better -- A 7.3T AI-Ready Corpus Built by a Model-Based HTML Parser
Ren Ma, Jiantao Qiu, Chao Xu +26
While web data quality is crucial for large language models, most curation efforts focus on filtering and deduplication,treating HTML-to-text extraction as a fixed pre-processing s…
Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM
Mengjie Liu, Jiahui Peng, Wenchang Ning +14
High-quality main content extraction from web pages is a critical prerequisite for constructing large-scale training corpora. While traditional heuristic extractors are efficient,…
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Junbo Niu, Zheng Liu, Zhuangcheng Gu +58
We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational effi…
WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset
Jiantao Qiu, Haijun Lv, Zhenjiang Jin +23
This paper presents WanJuan-CC, a safe and high-quality open-sourced English webtext dataset derived from Common Crawl data. The study addresses the challenges of constructing larg…