We introduce TACO, an open-source, large-scale code generation dataset, with
a focus on the optics of algorithms, designed to provide a more challenging
training dataset and evaluation benchmark in the field of code generation
models. TACO includes competition-level programming questions that are more
challenging, to enhance or evaluate problem understanding and reasoning
abilities in real-world programming scenarios. There are 25433 and 1000 coding
problems in training and test set, as well as up to 1.55 million diverse
solution answers. Moreover, each TACO problem includes several fine-grained
labels such as task topics, algorithms, programming skills, and difficulty
levels, providing a more precise reference for the training and evaluation of
code generation models. The dataset and evaluation scripts are available on
Hugging Face Hub (this https URL) and Github
(this https URL).

我们介绍了 TACO，这是一个开放源代码的大规模代码生成数据集，重点关注算法的光学，旨在提供更具挑战性的训练数据和评估基准，用于代码生成模型领域。TACO 包括竞赛级编程问题，旨在增强或评估实际编程场景中的问题理解和推理能力。数据集中包含了训练集和测试集中的 25433 个和 1000 个编码问题，以及高达 155 万个多样化的解决方案答案。此外，每个 TACO 问题都包含了任务主题、算法、编程技巧和难度级别等多个细粒度标签，为训练和评估代码生成模型提供更精确的参考。数据集和评估脚本可在 Hugging Face Hub 和 Github 上获取。