TACO Dataset TACO is a benchmark for code generation with 26443 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications. Key Update: We remove and modified some test cases in test set. Please update to use the newest version. Dataset Description Repository: https://github.com/FlagOpen/TACO/ Paper: TACO: Topics in Algorithmic COde generation dataset Leaderboard: Code Generation on CodeContests Point of Contact: Bo Wen Zhang Languages The dataset contains questions in English and code solutions in Python. Dataset Structure How to use it You can load and iterate through the dataset with the following two lines of code for the train split: Each sample consists of a programming problem formulation in English, some ground truth Python solutions, test cases that are defined by their inputs and outputs and function name if provided, as well as some metadata regarding the difficulty level (difficulty), topics of task (raw tags), algorithms (tags) as well as required programming skill types (skill types) of the problem and its source. If a sample has non empty input output feature, you can read it as a dictionary with keys inp…
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