This paper presents Topical, a novel deep neural network for repository level embeddings. Existing methods, reliant on natural language documentation or naive aggregation techniques, are outperformed by Topical's utilization of an attention mechanism. This mechanism generates repository-level representations from source code, full dependency graphs, and script level textual data. Trained on publicly accessible GitHub repositories, Topical surpasses multiple baselines in tasks such as repository auto-tagging, highlighting the attention mechanism's efficacy over traditional aggregation methods. Topical also demonstrates scalability and efficiency, making it a valuable contribution to repository-level representation computation. For further research, the accompanying tools, code, and training dataset are provided at: https://github.com/jpmorganchase/topical.

翻译：本文提出Topical，一种用于仓库级嵌入的新型深度神经网络。现有方法依赖自然语言文档或朴素聚合技术，而Topical通过使用注意力机制超越了这些方法。该机制能从源代码、完整依赖图及脚本级文本数据中生成仓库级表示。在公开可访问的GitHub仓库上训练后，Topical在仓库自动标注等任务中超越多个基线，凸显了注意力机制相比传统聚合方法的有效性。Topical还展现了可扩展性与高效性，使其成为仓库级表示计算领域的重要贡献。为促进进一步研究，配套工具、代码及训练数据集已开源，可访问：https://github.com/jpmorganchase/topical。