adaptNMT streamlines all processes involved in the development and deployment of RNN and Transformer neural translation models. As an open-source application, it is designed for both technical and non-technical users who work in the field of machine translation. Built upon the widely-adopted OpenNMT ecosystem, the application is particularly useful for new entrants to the field since the setup of the development environment and creation of train, validation and test splits is greatly simplified. Graphing, embedded within the application, illustrates the progress of model training, and SentencePiece is used for creating subword segmentation models. Hyperparameter customization is facilitated through an intuitive user interface, and a single-click model development approach has been implemented. Models developed by adaptNMT can be evaluated using a range of metrics, and deployed as a translation service within the application. To support eco-friendly research in the NLP space, a green report also flags the power consumption and kgCO\(_{2}\) emissions generated during model development. The application is freely available (http://github.com/adaptNMT).

AdaptNMT 简化了 RNN 和 Transformer 神经翻译模型的开发和部署所涉及的所有流程。作为一个开源应用程序，它是为机器翻译领域的技术和非技术用户而设计的。该应用程序基于广泛采用的 OpenNMT 生态系统而构建，对于该领域的新进入者特别有用，因为开发环境的设置以及训练、验证和测试分割的创建都得到了极大的简化。应用程序中嵌入的图形说明了模型训练的进度，SentencePiece 用于创建子词分割模型。通过直观的用户界面促进了超参数定制，并且已经实现了单击模型开发方法。由 AdaptNMT 开发的模型可以使用一系列指标进行评估，并在应用程序中部署为翻译服务。为了支持 NLP 领域的环保研究，绿色报告还标记了功耗和 kgCO\(_{2}\)模型开发过程中产生的排放。该应用程序可免费使用 (http://github.com/adaptNMT)。