Effective code documentation is essential in software engineering, playing a crucial role in maintaining code readability, facilitating collaboration, and ensuring long-term project sustainability. This paper presents a novel and carefully curated dataset aimed at advancing the automated generation of code comments for Java. The dataset comprises paired Java source code and corresponding comments, extracted from diverse open-source projects using a combination of Selenium for data crawling and JavaParser for syntactic analysis. The primary goal of this dataset is to empower the development of robust models capable of generating high-quality, context-aware code annotations, thus addressing a critical gap in automated software documentation. By providing a structured and scalable dataset, this work facilitates research at the intersection of AI and software engineering, promoting innovations in code comprehension and annotation tools. This resource is envisioned to enhance the capabilities of LLMs in generating accurate and relevant comments, ultimately fostering better coding practices, improving software maintenance, and contributing to the broader AI-driven automation of software engineering tasks.