  This paper describes the development of a new benchmark for machine
translation that provides training and test data for thousands of language
pairs covering over 500 languages and tools for creating state-of-the-art
translation models from that collection. The main goal is to trigger the
development of open translation tools and models with a much broader coverage
of the World's languages. Using the package it is possible to work on realistic
low-resource scenarios avoiding artificially reduced setups that are common
when demonstrating zero-shot or few-shot learning. For the first time, this
package provides a comprehensive collection of diverse data sets in hundreds of
languages with systematic language and script annotation and data splits to
extend the narrow coverage of existing benchmarks. Together with the data
release, we also provide a growing number of pre-trained baseline models for
individual language pairs and selected language groups.
