In light of this goal, adaptNMT has been developed This application is built upon the widelyadopted to provide users with a form of Explainable OpenNMT ecosystem, and is particularly Neural Machine Translation (XNMT). The useful for new entrants to the typical NMT process comprises several independent field, as it simplifies the setup of the development stages, including setting up the environment, environment and creation of train, preparing the dataset, training subword models, validation, and test splits. The application parameterizing and training the main models, evaluating offers a graphing feature that illustrates the and deploying them. By adopting a modular progress of model training, and employs approach, this framework has established an effective SentencePiece for creating subword segmentation NMT model development process that caters models. Furthermore, the application to both technical and non-technical practitioners in provides an intuitive user interface the field. To address the environmental impact of that facilitates hyperparameter customization.

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.