  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 (https://huggingface.co/datasets/BAAI/TACO) and Github
(https://github.com/FlagOpen/TACO).
