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.

我们通过引入一套用于改善低资源语言的机器翻译的资源集合，填补了自然语言处理领域的一个显著的空白，特别关注非洲语言。我们介绍了两个语言模型（Cheetah-1.2B和Cheetah-3.7B），分别具有12亿和37亿个参数。接下来，我们对上述模型进行微调，创建了一个名为toucan的非洲中心的机器翻译模型，支持156种非洲语言对。为了评估toucan，我们精心开发了一个广泛的机器翻译基准，命名为AfroLingu-MT，专门用于评估机器翻译。toucan在性能上显著优于其他模型，展示了其在非洲语言机器翻译方面的出色表现。最后，我们训练了一个新模型spBLEU-1K，以提高翻译评估指标，覆盖了614种非洲语言在内的1K种语言。该工作旨在推进自然语言处理领域，促进跨文化理解和知识交流，特别是在具有有限语言资源的地区，如非洲。Toucan项目的GitHub仓库位于此https网址。

Toucan：适用于150种非洲语言对的多对多翻译