DEEP：基于Docker的执行和评估平台
DEEP: Docker-based Execution and Evaluation Platform
AI总结：
DEEP是一款基于Docker的自动化评估平台，用于机器翻译和光学字符识别模型的执行、评分和性能分析，通过聚类算法和可视化工具提升评估效率与准确性。
摘要：
在研究中对几种系统进行比较评估是一项反复出现的任务。这是在决定使用哪个系统进行我们的工作之前的关键步骤，或者在我们的研究完成后，展示所得到模型潜力的关键步骤。此外，这也是竞争性、公开挑战评估的主要任务。我们提出的软件（DEEP）自动化了机器翻译和光学字符识别模型的执行和评分。此外，它可以轻松扩展到其他任务。DEEP准备接收Docker化的系统，运行它们（同时提取信息），并根据某些参考评估假设。通过这种方法，评估者可以更好地了解每个模型的性能。此外，该软件使用基于统计分析结果显著性的聚类算法，根据评估指标来确定每个模型的结果。因此，评估者能够识别出提案群中的性能聚类，并更好地理解其差异的重要性。此外，我们提供了一个可视化网页应用，以确保结果能够被充分理解和解释。最后，我们展示了DEEP的一个使用示例。
英文摘要：
Comparative evaluation of several systems is a recurrent task in researching. It is a key step before deciding which system to use for our work, or, once our research has been conducted, to demonstrate the potential of the resulting model. Furthermore, it is the main task of competitive, public challenges evaluation. Our proposed software (DEEP) automates both the execution and scoring of machine translation and optical character recognition models. Furthermore, it is easily extensible to other tasks. DEEP is prepared to receive dockerized systems, run them (extracting information at that same time), and assess hypothesis against some references. With this approach, evaluators can achieve a better understanding of the performance of each model. Moreover, the software uses a clustering algorithm based on a statistical analysis of the significance of the results yielded by each model, according to the evaluation metrics. As a result, evaluators are able to identify clusters of performance among the swarm of proposals and have a better understanding of the significance of their differences. Additionally, we offer a visualization web-app to ensure that the results can be adequately understood and interpreted. Finally, we present an exemplary case of use of DEEP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
