Classical Arabic represents a significant era, encompassing the golden age of Arab culture, philosophy, and scientific literature. With a broad consensus on the importance of translating these literatures to enrich knowledge dissemination across communities, the advent of large language models (LLMs) and translation systems offers promising tools to facilitate this goal. However, we have identified a scarcity of translation datasets in Classical Arabic, which are often limited in scope and topics, hindering the development of high-quality translation systems. In response, we present the ATHAR dataset, comprising 66,000 high-quality Classical Arabic to English translation samples that cover a wide array of subjects including science, culture, and philosophy. Furthermore, we assess the performance of current state-of-the-art LLMs under various settings, concluding that there is a need for such datasets in current systems. Our findings highlight how models can benefit from fine-tuning or incorporating this dataset into their pretraining pipelines. The dataset is publicly available on the HuggingFace Data Hub at \url{https://huggingface.co/datasets/mohamed-khalil/ATHAR}.

本研究解决了古典阿拉伯语翻译数据集稀缺的问题，此类数据集通常范围和主题有限，影响翻译系统的开发。我们提出了ATHAR数据集，包含66,000个高质量的古典阿拉伯语到英语的翻译样本，涉及科学、文化和哲学等广泛主题。研究结果表明，现有的大型语言模型在利用该数据集进行微调或纳入预训练管道时可显著提升性能。

ATHAR：一个高质量和多样化的古典阿拉伯语到英语翻译数据集