We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.

翻译：我们提出Azimuth,这是一个开放源码和易于使用的工具,用于对文本分类进行错误分析;与ML开发周期的其他阶段相比,例如模型培训和超参数调整,错误分析阶段的过程和工具不那么成熟;然而,这一阶段对于开发可靠和可靠的AI系统至关重要;为了使错误分析更加系统化,我们提议一种由Azimuth所推动的数据集分析和模型质量评估组成的方法;我们旨在帮助AI从业者发现和处理模型没有通过利用和整合一系列ML技术,如突出的地图、相似性、不确定性和行为分析等没有在同一个工具中加以推广的领域,我们的代码和文件可在 github.com/servicenow/azumuth查阅。