  We present AutoNMT, a framework to streamline the research of seq-to-seq
models by automating the data pipeline (i.e., file management, data
preprocessing, and exploratory analysis), automating experimentation in a
toolkit-agnostic manner, which allows users to use either their own models or
existing seq-to-seq toolkits such as Fairseq or OpenNMT, and finally,
automating the report generation (plots and summaries). Furthermore, this
library comes with its own seq-to-seq toolkit so that users can easily
customize it for non-standard tasks.

Computation and Language;Artificial Intelligence;Machine Learning