  Minimum Bayes risk (MBR) decoding is a decision rule of text generation tasks
that outperforms conventional maximum a posterior (MAP) decoding using beam
search by selecting high-quality outputs based on a utility function rather
than those with high-probability. Typically, it finds the most suitable
hypothesis from the set of hypotheses under the sampled pseudo-references. mbrs
is a library of MBR decoding, which can flexibly combine various metrics,
alternative expectation estimations, and algorithmic variants. It is designed
with a focus on speed measurement and calling count of code blocks,
transparency, reproducibility, and extensibility, which are essential for
researchers and developers. We published our mbrs as an MIT-licensed
open-source project, and the code is available on GitHub.
  GitHub: https://github.com/naist-nlp/mbrs
