AdaptNMT is an open-source application designed to streamline the development and deployment of RNN and Transformer neural translation models. It caters to both technical and non-technical users in the machine translation field. The application simplifies the setup of the development environment, offers intuitive user interfaces for hyperparameter customization, and provides a green report on power consumption and emissions during model development. Models developed by AdaptNMT can be evaluated using various metrics and deployed as a translation service within the application.
The tool is built upon OpenNMT ecosystem, offering features like graphing progress of model training, SentencePiece for subword segmentation models, and single-click model development approach. It allows running in local or hosted mode for infrastructure scaling. The system architecture includes initialization, pre-processing, environment setup, visualization, auto/custom NMT, training of subword model, main model training, evaluation, and deployment.
Key components explained include Recurrent Neural Network (RNN) architectures with LSTM models for sequence prediction problems like speech recognition and MT. Transformer architecture introduced attention mechanism for improved performance on NLP benchmarks. Attention function maps query-key-value pairs to compute weighted sum outputs based on compatibility functions.
The system also covers hyperparameter optimization methods like Grid Search and Random Search to customize machine learning models effectively. Evaluation metrics such as BLEU scores are used to measure translation quality along with PPL for language modeling effectiveness.
Environmental impact tracking was integrated into AdaptNMT with a 'green report' feature that logs kgCO2 emissions generated during model development. Future work includes integrating new transfer learning methods and developing adaptLLM for fine-tuning large language models focusing on low-resource languages.
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