Large Language Models (LLMs) play an ever-increasing role in the field of
Artificial Intelligence (AI)--not only for natural language processing but also
for code understanding and generation. To stimulate open and responsible
research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting
of permissively licensed source code in 30 programming languages. We describe
how we collect the full dataset, construct a permissively licensed subset,
present a data governance plan, discuss limitations, and show promising results
on text2code benchmarks by training 350M-parameter decoders on different Python
subsets. We find that (1) near-deduplicating the data significantly boosts
performance across all experiments, and (2) it is possible to match previously
reported HumanEval and MBPP performance using only permissively licensed data.
We make the dataset available at this https URL, provide a tool called
"Am I in The Stack" (this https URL) for developers
to search The Stack for copies of their code, and provide a process for code to
be removed from the dataset by following the instructions at
this https URL.

本文介绍了一个包含三十种编程语言的开源代码数据集 The Stack，以及如何构建、管理和使用该数据集来训练大型语言模型，通过在 Python 子集上训练 350M 参数解码器，在文本到代码的基准测试上取得了有前途的结果。