xCodeEval is one of the largest executable multilingual multitask benchmarks consisting of 17 programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation, and retrieval, and it employs an execution-based evaluation instead of traditional lexical approaches. It also provides a test-case-based multilingual code execution engine, ExecEval that supports all the programming languages in xCodeEval.
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PyTorrent contains 218,814 Python package libraries from PyPI and Anaconda environment. This is because earlier studies have shown that much of the code is redundant and Python packages from these environments are better in quality and are well-documented. PyTorrent enables users (such as data scientists, students, etc.) to build off the shelf machine learning models directly without spending months of effort on large infrastructure.
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This repository holds two datasets: one with both the original binaries and the code sections extracted from them (“full dataset”), and one with only the code sections (“only code sections”). The code sections were extracted by carving out sections of the binary that were marked as executable. The binaries were scraped from Debian repositories.
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TACO (Topics in Algorithmic COde generation dataset) is a dataset focused on algorithmic code generation, designed to provide a more challenging training dataset and evaluation benchmark for the code generation model field. The dataset consists of programming competition problems that are more difficult and closer to real programming scenarios. It emphasizes improving or evaluating the model's understanding and reasoning abilities in practical application scenarios, rather than just implementing predefined function functionalities.
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