  We address a notable gap in Natural Language Processing (NLP) by introducing
a collection of resources designed to improve Machine Translation (MT) for
low-resource languages, with a specific focus on African languages. First, we
introduce two language models (LMs), Cheetah-1.2B and Cheetah-3.7B, with 1.2
billion and 3.7 billion parameters respectively. Next, we finetune the
aforementioned models to create toucan, an Afrocentric machine translation
model designed to support 156 African language pairs. To evaluate Toucan, we
carefully develop an extensive machine translation benchmark, dubbed
AfroLingu-MT, tailored for evaluating machine translation. Toucan significantly
outperforms other models, showcasing its remarkable performance on MT for
African languages. Finally, we train a new model, spBLEU-1K, to enhance
translation evaluation metrics, covering 1K languages, including 614 African
languages. This work aims to advance the field of NLP, fostering cross-cultural
understanding and knowledge exchange, particularly in regions with limited
language resources such as Africa. The GitHub repository for the Toucan project
is available at https://github.com/UBC-NLP/Toucan.
